Lectures on Measure Theory, Probability, and Stochastic Processes

Measure-Probability: Comprehensive overview of Measure Theory and Probability Theory, ideal for students seeking courses or tutorials in intro to measure theory, advanced probability, and real analysis.

This webpage offers an extensive collection of resources for mastering Measure Theory, Probability, Stochastic Processes, and Markov Chains. Explore PDFs, video lectures, and meticulously crafted exercises with step-by-step solutions to deepen your understanding.

We introduce a unified approach to Measure Theory, Probability, Stochastic Processes, and Markov Chains, organized under the cohesive framework of ‘Measure-Probability theory’. This foundational understanding underpins Statistics Theory, Machine Learning, Information Theory, Queueing Theory and Point Processes.

Whether you’re seeking an introduction or advanced courses, our carefully selected online resources provide comprehensive tutorials, notes, and exercises to support your learning journey. support your learning journey.

1. Measure Theory

Measure Theory Course and Exercises:

Measure Theory is a cornerstone of modern mathematics that underpins crucial concepts in probability, integration theory, and beyond. This primer serves as your comprehensive guide to understanding the fundamental principles and applications of measure theory. The lecture consists of two sections:

  1. Measurability: Kicking off with the fundamental concept of measurability, we delve into algebra and σ-algebra, elucidating the notions of measurable sets, generated σ-algebras, and the pivotal Borel σ-algebra. We then shift focus to measurable functions, discussing their properties such as measurability at the limit, and introducing the concept of simple measurable functions alongside the indispensable simple approximation theorem.
  2. Measures: This section embarks on a rigorous examination of measures, commencing with their definition and the establishment of essential lemmas delineating their properties. Through insightful examples, including the Dirac measure and the weighted counting measure, we elucidate the concepts of finite, probability, and σ-finite measures. The continuity of measures is rigorously addressed through a pivotal theorem. Further, we delve into extension and uniqueness of measures, exploring the extension of σ-finite measures from algebras and from semirings. The lecture progresses to unveil the relationship between measures and cumulative distribution functions, alongside elucidating the concept of « almost everywhere » through rigorous definitions and examples. The existence and uniqueness of the Lebesgue measure on ℝ^n are established, laying a robust foundation for further exploration.

Course Outline:
1 Measurability
    1.1 Algebra and σ-algebra
    1.2 Measurable functions
2 Measures
    2.1 Extension and uniqueness of measures
    2.2 Cumulative distribution function
    2.3 Almost everywhere
    2.4 Lebesgue measure

Whether you’re looking for comprehensive textbooks, engaging online courses, or challenging exercises with detailed solutions, we have curated a collection tailored to your needs. Dive into the intricacies of measurable sets and functions, explore the nuances of Borel measurable functions, or embark on a concise yet comprehensive introduction to measure theory. With our curated resources, mastering measure theory and its applications, especially in the realm of probability, has never been more accessible.

2. Lebesgue Integral

Lebesgue Integral Course and Exercises:

The Lebesgue integral offers a profound extension of the classical Riemann integral that revolutionized the study of integration theory. In this comprehensive primer, we embark on a rigorous exploration of the construction and properties of the Lebesgue integral, illuminating its foundational concepts and advanced techniques. By delving into topics ranging from the exchange of integral and limit operations to the intricate theory of Lp spaces, we aim to provide readers with a deep understanding of this essential mathematical tool and its wide-ranging applications across various domains. The lecture comprises four sections:

  1. Construction of the Lebesgue Integral: This foundational section lays the groundwork for understanding the Lebesgue integral by detailing its construction. We explore essential concepts such as the exchange of integral and limit operations, exemplified through the Monotone Convergence Theorem, Fatou’s Lemma, and the Dominated Convergence Theorem. These results establish the convergence properties of integrals for increasingly complex functions, paving the way for a more flexible and comprehensive theory of integration. We present finally the Change of Variable formula, which provides a rigorous framework for transforming integrals under mappings between different spaces.
  2. Fubini’s Theorem: Fubini’s Theorem, a cornerstone of Lebesgue integration theory, is the focus of this section. We investigate the existence and uniqueness of product measures and delve into the Fubini-Tonelli theorem, which provides conditions under which the order of integration can be interchanged. By elucidating the structure of integration over product spaces, this theorem offers valuable insights into the interplay between measures and functions in higher-dimensional settings. Fubini’s Theorem provides also the necessary framework for applying integration by parts to measures and CDFs by establishing conditions under which the order of integration can be interchanged.
  3. Radon-Nikodym Theorem: The Radon-Nikodym theorem is central to understanding the relationship between measures and functions. In this section, we explore the product of a measure by a function and delve into the notion of absolute continuity. By establishing the Radon-Nikodym theorem, we unveil the existence of a derivative that characterizes the relationship between measures, shedding light on the underlying structure of measure spaces. The Lebesgue decomposition of measures, a direct application of the Radon-Nikodym Theorem, provides a systematic method for breaking measures into singular and absolutely continuous components. This decomposition unveils the nuanced structure of measure spaces, shedding light on the distributional properties of measures.
  4. Lp Spaces: Finally, we turn our attention to Lp spaces, which generalize the concept of integrability to accommodate functions with varying degrees of growth. This section explores fundamental concepts such as essentially bounded functions, Minkowski’s inequality, Holder’s inequality, and the celebrated Riesz-Fischer theorem. Moreover, we delve into the theory of Hilbert space L2 and discuss convergence properties within Lp spaces, highlighting the rich analytical structure inherent in these function spaces.

Course Outline:
1 Construction of the Lebesgue integral
    1.1 Exchange of integral and limit
    1.2 Change of variable
2 Fubini’s theorem
    2.1 Integration by parts for measures
3 Radon-Nikodym theorem
    3.1 Lebesgue decomposition
4 Lp spaces

Whether you’re a student delving into measure theory for the first time or a seasoned mathematician seeking a refresher, our resource provides a clear and concise introduction to Lebesgue integration. Download our introductory PDF for a comprehensive overview and unlock the mysteries of Lebesgue integration today.

3. Probability Theory [Measure-Probability]

Probability Theory Course and Exercises:

In this course, we will explore how probability theory serves as a cornerstone for understanding uncertainty and randomness in diverse fields such as mathematics, statistics, and beyond. Through a structured approach, we will unravel the theoretical underpinnings of probability, starting with its foundations in measure theory and progressing to advanced topics such as random variables, distribution functions, and convergence theorems. By the end of this course, you will have gained a comprehensive understanding of probability theory and its practical applications, equipping you with valuable insights for analyzing and interpreting uncertain events. The lecture consists of three sections: 

  1. Probability Space: In this initial section, we delve into the foundational concepts of probability theory, elucidating how it intertwines with measure theory. We begin by defining the notion of a probability space, encompassing essential elements such as the probability measure, events, and the concept of « almost surely » within the realm of probability theory. This sets the stage for a comprehensive understanding of the probabilistic framework that underpins subsequent discussions.
  2. Random Variable: Building upon the foundational groundwork laid in the first section, we explore the intricacies of random variables and their associated probability distributions. Within this segment, we cover the definition and characteristics of random variables, including the σ-algebra generated by them. Furthermore, we delve into the expectation of random variables, unraveling its definition, key properties, and its representation as an integral. Additionally, we discuss moments, characteristic functions, cumulative distribution functions (CDFs), and probability density functions (PDFs), elucidating their significance within the context of random variables. The notion of independence among random variables is also elucidated, along with pertinent definitions and associated lemmas.
  3. Monotone and Dominated Convergence Theorems for Random Variables: The final section of our course delves into convergence theorems for random variables, providing crucial insights into the behavior of sequences of random variables. Specifically, we explore the monotone convergence theorem and its applicability in the context of random variables, shedding light on its significance and implications. Moreover, we delve into the dominated convergence theorem, offering a comprehensive understanding of its utility and relevance within the realm of probability theory. These convergence theorems serve as powerful tools for analyzing the behavior of random variables in various scenarios, providing valuable insights into their convergence properties and facilitating the rigorous study of probabilistic phenomena.

Course Outline:
1 Probability Space
2 Random Variable
      2.1 Expectation of Random Variable
      2.2 Moments and Characteristic Function
      2.3 CDF and PDF
      2.4 Independence
3 Montone and Dominated Convergence for Random Variables
      3.1 Monotone Convergence for Random Variables
      3.2 Dominated Convergence for Random Variables

Whether you’re seeking a foundational understanding or delving into advanced topics, our course caters to all levels of expertise. From exploring probability spaces to dissecting random variables, characteristic functions, and probability density functions (PDFs), we cover it all. Gain insights into expectation, cumulative distribution functions (CDFs), and the intricate relationship between measure theory and probability theory. Join us for an introduction to measure theoretic probability and propel your understanding to new heights.

4. Discrete Random Variables and their Transform

Discrete Random Variables Course and Exercises:

In this course, we’ll be focusing on discrete random variables, starting with the fundamental types such as Bernoulli, Binomial, Geometric, and Poisson. We will examine their definitions, properties, and applications. Following that, we’ll introduce the concept of the probability generating function, discussing its definition, properties, and how it’s used to analyze distributions and moments. The lecture is structured into two main sections:

  1. Fundamental Discrete Random Variables: This section of the lecture delves into the core types of discrete random variables that form the foundation of probabilistic analysis and stochastic processes. We begin by exploring the Bernoulli random variable, which represents the simplest form of a random process with only two possible outcomes. Building on this, we examine the Binomial random variable, which extends the Bernoulli process to multiple trials, allowing us to model the number of successes in a fixed number of independent trials. Next, we consider the Geometric random variable, which focuses on the number of trials needed to achieve the first success in a series of Bernoulli trials, introducing the concept of ‘waiting time’ in probability theory. Lastly, the Poisson random variable is introduced as a model for the number of events occurring in a fixed interval of time or space, under the assumption that these events happen with a known constant mean rate and independently of the time since the last event.
  2. Probability Generating Function: In the second part of the lecture, we explore the probability generating function (PGF), a powerful tool in the analysis of discrete random variables. We start with the definition and fundamental properties of the PGF, explaining how it encapsulates all the probabilistic information of a discrete random variable. Through examples, we illustrate how to construct and interpret the PGF for different types of discrete distributions. We then discuss how the PGF can be utilized to analyze the distribution of a random variable, including deriving its factorial moments, which are essential for understanding the variable’s mean, variance, and higher-order moments. The concept of a random sum of random variables is introduced, demonstrating the utility of the PGF in solving complex problems involving sums of an arbitrary number of random variables. Finally, we conclude with an examination of the monotonicity and convexity properties of the PGF, which provide insights into the behavior of the underlying random variable and have implications for various applications in probability and statistics.

Course Outline:
1 Fundamental discrete random variables
    1.1 Bernoulli random variable
    1.2 Binomial random variable
    1.3 Geometric random variable
    1.4 Poisson random variable
2 Probability generating function
    2.1 Definition and properties
    2.2 Examples of probability generating functions
    2.3 Probability generating function and distribution analysis
    2.4 Factorial moments from probability generating function
    2.5 Random sum of random variables
    2.6 Monotonicity and convexity of the probability generating function

To further enhance your understanding of discrete random variables and their applications, our comprehensive lecture provides an in-depth analysis of their properties, including the expected value, variance, and higher-order moments. We cover a range of distributions such as the Bernoulli, Binomial, Geometric, and Poisson, each with its unique set of parameters and implications for statistical modeling. Our materials include a detailed exploration of the probability generating function, a cornerstone concept for distribution analysis, which facilitates the computation of expected values and factorial moments. We also provide insights into the behavior of sums of discrete random variables, particularly Bernoulli and Poisson, and how their combined distributions can be derived.

5. Continous Random Variables and their Transforms

Continuous Random Variables Course and Exercises:

In this lecture, we will examine the core concepts of continuous random variables, focusing on the Gaussian and Exponential distributions. We will then discuss the moment generating function, its properties, and its role in characterizing distributions. The characteristic function and its applications in probability theory will be analyzed, alongside Lévy’s inversion formula. Finally, we will explore the Laplace transform and its utility in expanding and solving problems involving continuous random variables. Specifically, we will cover:

  1. Fundamental Continuous Random Variables: This section introduces the foundational building blocks of continuous distributions, focusing on two pivotal types: the Gaussian random variable, renowned for its bell curve and central role in the Central Limit Theorem, and the Exponential random variable, which is crucial in modeling time until an event, such as decay or failure rates. 
  2. Moment Generating Function: We will explore the moment generating function (MGF), starting with its formal definition and discussing the conditions for its existence through the radius of convergence. The session will then proceed to examine the MGF’s infinite series expansion, which provides a powerful tool for deriving moments and understanding the distribution of a random variable.
  3. Characteristic Function: The third segment of the lecture will cover the characteristic function, a Fourier transform of the probability density function. We will discuss its differentiability and the implications of its finite expansion, followed by an explanation of Lévy’s inversion formula, which is instrumental in probability theory. The session will conclude with an analysis of the infinite expansion of the characteristic function and its applications.
  4. Laplace Transform: In the final part of the lecture, we will discuss the Laplace transform, another integral transform that is particularly useful in solving differential equations and analyzing systems. We will delve into its differentiability, finite expansion, and the conditions under which these hold. The lecture will culminate with an examination of the infinite expansion of the Laplace transform and its relevance to continuous random variables.

Course Outline:
1 Fundamental continuous random variables
    1.1 Gaussian random variable
    1.2 Exponential random variable
2 Moment generating function
    2.1 Definition and radius of convergence
    2.2 Infinite expansion of the moment generating function
3 Characteristic function
    3.1 Differentiability and finite expansion of characteristic function
    3.2 Lévy’s inversion formula
    3.3 Characteristic function of random vectors
    3.4 Infinite expansion of the characteristic function
4 Laplace transform
    4.1 Differentiability and finite expansion of Laplace transform
    4.2 Infinite expansion of the Laplace transform

Our lecture offers detailed explanations and examples on various aspects of continuous distributions and their transforms. From understanding the fundamentals of Gaussian and Exponential random variables to exploring the intricacies of Moment generating functions and Laplace transforms, our resources delve into the core concepts essential for grasping probability distributions in continuous domains. Whether you seek insights into Gaussian random variables or wish to unravel the mysteries of Lévy’s inversion formula, our meticulously crafted content caters to learners at all levels. With downloadable PDF lecture notes readily available, mastering continuous random variables and probability distributions has never been more accessible.

6. Random Vectors and Multivariate Gaussian Distributions

Random Vectors and Multivariate Gaussian Distributions Course: 

In this lecture, we will explore the intricate mathematical structures that underpin the behavior of random vectors and delve into the specific characteristics of Gaussian distributions. The lecture is structured into three main sections:

  1. Characteristic Functions of Random Vectors: This section delves into the mathematical backbone of random vectors through the lens of characteristic functions. It begins with an exploration of Lévy’s Inversion Formula, which is pivotal for retreiving the distribution of random vectors from their characteristic functions. The section then transitions to discussing the Independence Criterion, which provides a method to determine the statistical independence of components within random vectors.
  2. Square-Integrable Random Vectors: Focusing on square-integrable random vectors, this part of the lecture introduces the concept of the Covariance Matrix, a critical tool for understanding the variance and correlation structure of random vectors. It also covers Degenerate Random Vectors, which are pivotal in revealing the limitations and special cases in multivariate distributions. Lastly, the section discusses Affine Transformations, demonstrating how linear transformations affect the properties of random vectors, which is essential for practical applications in statistics and data analysis.
  3. Gaussian Random Vectors: This section is dedicated to Gaussian Random Vectors, beginning with their definition and characteristic function, which are crucial for understanding their behavior and properties. It then examines the relationship between Independence and Uncorrelation, clarifying common misconceptions and providing insights into the structure of Gaussian vectors. The lecture concludes with an analysis of the Probability Density Function of a Nondegenerate Gaussian Vector, offering a comprehensive understanding of how these vectors behave in a multidimensional space.
  4. Complex Gaussian Random Vectors: The final section begins with the fundamentals of complex random vectors. It progresses to explore the properties of symmetric complex Gaussian vectors and establishes criteria for the independence of jointly CN random vectors. The discussion then moves to the spectral decomposition and characterization of CN vectors, providing a deeper understanding of their structural properties. The lecture addresses the real representations of complex vectors and matrices, which is crucial for practical computations and theoretical derivations. It also examines the covariance and linear combinations of CN random vectors, highlighting their interrelationships and dependencies. Finally, the focus shifts to the probability density function of CN random vectors, encapsulating their distributional characteristics and how they are influenced by their covariance structures.

Course Outline:
1 Characteristic Functions of Random Vectors
  1.1 Lévy’s Inversion Formula for Random Vectors
  1.2 Independence Criterion
2 Square-Integrable Random Vectors
  2.1 Covariance Matrix
  2.2 Degenerate Random Vectors
  2.3 Affine Transformations
3 Gaussian Random Vectors
  3.1 Definition and Characteristic Function
  3.2 Independence Versus Uncorrelation
  3.3 Probability Density Function of a Nondegenerate Gaussian Vector
4 Complex Gaussian Random Vectors
  4.1 Basics of Complex Random Vectors
  4.2 Symmetric Complex Gaussian Vector
  4.3 Criterion for Independence of Jointly CN Random Vectors
  4.4 Spectral Decomposition and Characterization of CN Vectors
  4.5 Real Representations of Complex Vectors and Matrices
  4.6 Covariance and Linear Combinations of CN Random Vectors
  4.7 Probability Density Function of CN Random Vectors

This lecture meticulously covers a range of critical topics including the probability density function of multivariate normal distributions, the role of characteristic functions in identifying the behavior of random vectors, and the practical implications of the covariance matrix in statistical analysis. We will also delve into specialized topics such as Lévy’s inversion formula, which is essential for reconstructing distributions from their characteristic functions, and explore the independence criterion that helps in determining the independence of vector components. Additionally, the lecture will clarify concepts related to square-integrable vectors, degenerate vectors, and the effects of affine transformations on distribution properties. This comprehensive approach ensures a robust grasp of Gaussian distribution and its applications in various fields.

To further enhance your understanding of complex Gaussian random vectors, this lecture meticulously covers various essential topics. We delve into the probability density function and covariance matrix of CN random vectors, crucial for grasping their distribution and variability. Additionally, the lecture explores the spectral decomposition, which breaks down these vectors into more manageable components, and the concept of symmetric Gaussian vectors, which play a pivotal role in complex vector theory. For those interested in the statistical relationships between vectors, our discussion on the independence of random vectors provides fundamental insights. Lastly, we address the real representations of complex vectors, a topic vital for applying complex vector theory in real-world scenarios. 

7. Convergence of Sequences of Random Variables

Convergence of Sequences of Random Variables Course:

In this lesson, we will embark on a rigorous exploration of the various modes of convergence for random variables, a cornerstone concept in probability theory with profound implications across statistics and stochastic processes. We will begin by examining almost-sure convergence, including the Strong Law of Large Numbers and the Borel-Cantelli lemmas, which lay the groundwork for understanding the behavior of sequences of random variables. We will then transition to convergence in probability and its relationship with continuous mappings, followed by a detailed look at convergence in quadratic mean.

The lesson will progress to an in-depth analysis of weak convergence, also known as convergence in distribution, where we will discuss the Portmanteau theorem, the Continuous Mapping Theorem for weak convergence, the Characteristic Function Criterion, and the pivotal Central Limit Theorem. We will also investigate the intricate connections between these convergence types, the concept of stochastic order, and the critical role of tightness in probability measures, culminating with Prohorov’s Theorem. The lecture is comprises seven main sections:

  1. Almost-sure convergence: This section delves into the concept of almost-sure convergence, beginning with the Strong Law of Large Numbers, which establishes the conditions under which the averages of random variables converge to their expected value almost surely. We then explore the Borel-Cantelli lemmas, which provide a probabilistic framework for understanding the occurrence of events almost surely. The section concludes with a discussion on various conditions that guarantee almost-sure convergence, offering a comprehensive overview of this robust form of convergence.
  2. Convergence in probability: The focus shifts to convergence in probability, a weaker form of convergence compared to almost-sure convergence. This section introduces the Continuous Mapping Theorem for convergence in probability, which extends the convergence properties of random variables to functions of those random variables, thereby broadening the applicability of convergence concepts in probability theory.
  3. Convergence in quadratic mean: This part of the lecture presents convergence in quadratic mean, also known as L2 convergence, which requires that the mean squared difference between random variables and their limit converges to zero. This type of convergence is particularly important in the context of mean-square error analysis and signal processing.
  4. Weak convergence – Convergence in distribution: The lecture progresses to weak convergence, also known as convergence in distribution. Key topics include the Portmanteau theorem, which offers various equivalent definitions of weak convergence, and the Continuous Mapping Theorem for weak convergence, which, similar to its counterpart in probability, relates the convergence of random variables to their transformations. The section also covers the Characteristic Function Criterion, a powerful tool for proving convergence in distribution, and concludes with the Central Limit Theorem, a fundamental result in probability theory that explains the emergence of the normal distribution in large samples.
  5. Connections between convergence types: This section synthesizes the different types of convergence discussed in the lecture, elucidating the relationships and hierarchies among them. It provides a structured framework for understanding how one form of convergence may imply another and under what circumstances such implications hold.
  6. Stochastic order: The lecture then explores the concept of stochastic order, a probabilistic technique for comparing the magnitudes of random variables. This section examines how stochastic orders can be used to describe the asymptotic behavior of sequences of random variables, providing insights into their growth rates and variability.
  7. Tightness of probability measures: The final section addresses the concept of tightness and its significance in the convergence of probability measures. It begins with a discussion on tightness and uniform tightness, followed by lemmas that establish the foundational properties of tight measures. The lecture culminates with Prohorov’s Theorem, which links tightness with weak convergence and is particularly crucial in the study of weak convergence in infinite-dimensional spaces.

Couse Outline:
1 Almost-sure convergence
    1.1 Strong law of large numbers
    1.2 Borel-Cantelli lemmas
    1.3 Conditions for almost-sure convergence
2 Convergence in probability
    2.1 Continuous mapping theorem for convergence in probability
3 Convergence in quadratic mean
4 Weak convergence – Convergence in distribution
    4.1 Portmanteau theorem
    4.2 Continuous mapping theorem for weak convergence
    4.3 Characteristic function criterion
    4.4 Central limit theorem
5 Connections between convergence types
6 Stochastic order
7 Tightness of probability measures
    7.1 Tightness and uniform tightness
    7.2 Lemmas on Tightness
    7.3 Prohorov’s Theorem

This lecture provides a solid foundation for understanding the subtleties of key topics such as almost-sure convergence, convergence in probability, and convergence in quadratic mean. We delve into the heart of weak convergence, also known as convergence in distribution, and discuss its profound implications through the strong law of large numbers and the Borel-Cantelli lemmas. Our expertly crafted content elucidates the continuous mapping theorem and its applications, the significance of the characteristic function in the central limit theorem, and the intricate concept of stochastic order. Furthermore, we unravel the complexities of tightness of probability measures, guided by the pivotal Prohorov’s theorem and the insightful Portmanteau theorem.

8. Stochastic Processes

Stochastic Processes Course:

Stochastic processes are mathematical models that depict the evolution of systems over time (or space) through probabilistic mechanisms. These models are essential for interpreting phenomena where outcomes are unpredictable and subject to random influences. Stochastic processes are widely applied in diverse fields including data science, physics, biology, and economics, providing critical insights into complex, dynamic systems. This lesson provides a comprehensive exploration of the theoretical underpinnings and practical applications of stochastic processes, structured into four distinct sections:

  1. Foundations and Characterizations of Stochastic Processes: We begin by defining stochastic processes and discussing the concept of independence within these processes. This foundation sets the stage for understanding finite-dimensional distributions (fidis), essential for grasping the behavior of processes across various dimensions. We will delve into Kolmogorov’s Extension Theorem, which provides a method for constructing a process from its finite-dimensional distributions. The section will also cover the analysis of sample paths, exploring their properties and behaviors, and conclude with a discussion on second-order stochastic processes, focusing on their mean and covariance functions.
  2. Strict and Wide-Sense Stationarity: This section focuses on the concepts of stationarity in stochastic processes. We will differentiate between strict stationarity, which requires the joint distribution of any set of points to be invariant under time shifts, and wide-sense stationarity, which relaxes this to only require invariance in the mean and autocovariance functions. These concepts are pivotal for understanding the long-term behavior and stability of stochastic processes.
  3. Measurability and Stochastic Integral: We will explore the measurability of stochastic processes, an essential property ensuring that the processes behave well under integration and other analytical operations. Following this, we will introduce the stochastic integral, a critical tool in stochastic calculus, which extends the concept of integration to functions of stochastic processes. This part is fundamental for developing models involving differential equations driven by stochastic processes.
  4. Gaussian and Wiener Processes: The final section of the lecture will cover Gaussian processes, starting with their definition and characteristic functions. We will discuss the conditions under which Gaussian processes exist and their stationarity properties. The lecture will conclude with an in-depth look at the Wiener process, also known as Brownian motion, which is a cornerstone of stochastic modeling in various fields, including finance and physics.

Course Outline:
1 Foundations and Characterizations of Stochastic Processes
    1.1 Definition and Independence of Stochastic Processes
    1.2 Finite-Dimensional Distributions (fidis)
    1.3 Kolmogorov’s Extension Theorem
    1.4 Sample Paths of a Stochastic Process
    1.5 Second-Order Stochastic Process
2 Strict and Wide-Sense Stationarity
    2.1 Stationary Stochastic Processes
    2.2 Wide-Sense Stationarity
3 Measurability and Stochastic Integral
    3.1 Measurable Stochastic Processes
    3.2 Stochastic Integral
4 Gaussian and Wiener Processes
    4.1 Definition and Characteristic Function of Gaussian Process
    4.2 Existence and Stationarity of Gaussian Process
    4.3 Wiener process – Brownian motion

To further enhance your understanding of stochastic processes, our lecture will provide detailed insights into each topic, ensuring clarity and depth. Whether you are exploring the definition of stochastic processes or delving into complex topics like Kolmogorov’s Extension Theorem and finite-dimensional distributions, this lecture is designed to address all key areas comprehensively. We will also cover the practical implications and theoretical underpinnings of independence in stochastic processes, sample paths, and second-order stochastic processes. For those interested in the dynamics of stationarity and wide-sense stationarity, as well as the mathematical tools such as the stochastic integral, our content is tailored to provide a thorough understanding. Additionally, we will explore the critical roles of Gaussian processes, the Wiener process, and Brownian motion in stochastic modeling, making this lecture indispensable for anyone looking to deepen their knowledge in these areas. This comprehensive approach ensures that our page becomes a pivotal resource for students and professionals alike, seeking to enhance their expertise in stochastic processes.

9. Conditional Expectation and MMSE Estimation

Conditional Expectation and MMSE Estimation Course:

This lecture provides an in-depth exploration of conditional expectation and MMSE estimation, two fundamental concepts in probability theory and statistical estimation. The content is structured to build a comprehensive understanding, starting from the basics and progressing to more advanced topics. The lecture is divided into two main sections, each focusing on a critical aspect of these concepts.

  1. Conditional Expectation: This section delves into the concept of conditional expectation, starting with the fundamentals of conditioning with respect to a sigma-algebra and a random variable. It explores the properties of conditional expectation, providing a solid theoretical foundation. The section also covers important convergence theorems, such as monotone and dominated convergence, in the context of conditional expectation. Additionally, it distinguishes between conditional expectations for discrete and continuous variables and examines the special case of conditional expectation in Gaussian distributions.
  2. MMSE Estimation: The second section focuses on Minimum Mean Square Error (MMSE) estimation, highlighting its relationship with conditional expectation. It begins by comparing MMSE estimation to conditional expectation, emphasizing their similarities. The section then introduces linear MMSE estimation, discussing its practical applications and advantages in various scenarios. This comprehensive exploration provides a deep understanding of MMSE estimation techniques and their relevance in statistical analysis and signal processing.

Course Outline:
1 Conditional Expectation
    1.1 Conditioning With Respect to a Sigma-Algebra
    1.2 Conditioning With Respect to a Random Variable
    1.3 Properties of Conditional Expectation
    1.4 Monotone and Dominated Convergence for Conditional Expectation
    1.5 Conditional Expectation for Discrete and Continuous Variables
    1.6 Conditional Expectation in Gaussian Case
2 MMSE Estimation
    2.1 MMSE Estimation vs. Conditional Expectation
    2.2 Linear MMSE Estimation

To further enhance your understanding of these concepts, this lecture will provide detailed explanations and practical examples that illustrate the application of conditional expectation and MMSE estimation in various fields. By exploring the properties of conditional expectation, including the monotone and dominated convergence theorems, and examining both discrete and continuous variables, you will gain a comprehensive grasp of these essential statistical tools. Additionally, the lecture will delve into the Gaussian distributions and linear MMSE estimation, offering insights into their significance in statistical estimation and random variable conditioning.

10. Martingales and Ergodic Theory

Martingales and Ergodic Theory Course:

In this lecture, we will explore the foundational concepts and theorems of martingales and ergodic theory, two pivotal areas in probability and dynamical systems. The lecture is structured into two main sections, each providing a comprehensive overview of the respective topics.

  1. We begin with an introduction to martingales, focusing on their definition and basic properties. This section will cover the formal definition of martingales, highlighting their significance in stochastic processes and their key characteristics. Following this, we will delve into the optional stopping theorem and martingale convergence theorems, which are crucial for understanding the behavior of martingales under various conditions. To solidify these concepts, we will examine several examples of martingales, illustrating their application in different probabilistic contexts.

  2. The second section of the lecture is dedicated to ergodic theory. We will start by discussing the notions of flow, shift, and compatibility, which are essential for understanding the dynamics of systems over time. Next, we will introduce the stationary framework, providing a foundation for analyzing systems that exhibit statistical regularity. A key highlight of this section is Birkhoff’s Pointwise Ergodic Theorem, a fundamental result that connects individual trajectories with long-term statistical behavior. Finally, we will explore the ergodic stationary framework, offering insights into the behavior of systems that are both stationary and ergodic.

Course Outline:
1 Martingales
    1.1 Definition and Basic Properties of Martingales
    1.2 Optional Stopping and Martingale Convergence Theorems
    1.3 Examples of Martingales
2 Ergodic Theory
    2.1 Flow, Shift and Compatibility
    2.2 Stationary Framework
    2.3 Birkhoff’s Pointwise Ergodic Theorem
    2.4 Ergodic Stationary Framework

Martingales and ergodic theory are essential topics in the study of stochastic processes. Understanding the martingale convergence theorem and the optional stopping theorem is crucial for anyone delving into these areas. Additionally, the stationary framework and the concepts of flow and shift in ergodic theory provide a deeper insight into the dynamics of systems. This lecture will also cover Birkhoff’s pointwise ergodic theorem and provide various examples of martingales to illustrate these principles. By exploring the ergodic stationary framework, you will gain a comprehensive understanding of these interconnected topics.

Exercises with Solutions on Measure Theory, Probability, Stochastic Processes, and Markov Chains

Here is a collection of exercises meticulously crafted to serve as practical applications of the concepts elucidated in the Measure Theory, Probability, Stochastic Processes, and Markov Chains lectures. By engaging with these exercises, students can enhance their analytical skills and develop a nuanced understanding of measure and probability principles, thereby solidifying their grasp of advanced mathematical concepts.

1. Exercises on Measure Theory

Exercises with Solutions:

  1. Exercises on Measurability: Delving into specific topics, such as measurability with respect to the gross σ-algebra and the measurability of the set of convergence points, the exercises provide a platform for students to apply theoretical knowledge to real-world scenarios. Moreover, the exercises culminate in the rigorous proof of the simple approximation theorem, offering a comprehensive exploration of foundational theorems within measure theory.
  2. Exercises on Measures: Beginning with an exploration of measures of set differences and continuity characterizations of measures, the exercises progress to analyze properties of cumulative distribution functions and elucidate disparities between continuous functions. Moreover, the exercises scrutinize the relationship between measurability and boundedness, shedding light on crucial distinctions within measure theory.

2. Exercises on Lebesgue Integral

Exercices with Solutions: Beginning with the exploration of integrals with respect to specific measures such as the Dirac measure and counting measure, the exercises elucidate the nuanced relationships between integrability and measure spaces. Furthermore, the exercises delve into key inequalities, including Markov’s inequality, and examine the notions of finite and zero integrals.

3. Exercises on Probability Theory

Exercices with Solutions: These exercises delve into various facets of probability theory, encompassing topics such as the measurability of convergence points, characteristics of probability density functions (PDFs), and properties inherent in cumulative distribution functions (CDFs). The exercises also explore scenarios involving the vanishing of PDFs, the derivation of PDFs for the sum of independent and identically distributed (i.i.d) uniform variables, and the distribution of uniform random variables on a disk.

Moreover, the exercises cover a diverse array of topics including the Markov property, the characterization of conditional independence, methods for minimizing dispersion around the mean, and the determination of the standard deviation of empirical mean. Additionally, an exercise on Poincaré’s formula is provided.

Furthermore, these exercises include the derivation of probability density functions (PDFs) for the product of independent and identically distributed (i.i.d.) random variables, the determination of the maximum value among i.i.d. variables, and the calculation of PDFs for the ratio of uniform random variables. Finally, the exercises entail the rigorous proof of the Schwarz inequality, a foundational result in mathematical analysis.

4. Exercises on Discrete Random Variables

Exercises with Solutions: The exercises encompass a range of topics including the derivation and application of the second moment of a geometric distribution, the exploration of the memoryless property of the geometric distribution, the investigation into the sum of two geometric random variables, and the calculation of the expected value of the factorial of a Poisson random variable. 

5. Exercises on Continuous Random Variables

Exercises with Solutions: This series of exercises is designed to deepen understanding of continuous random variables and their transforms. These exercises cover a diverse range of topics, beginning with the analysis of moments associated with Gaussian random variables, a fundamental concept in probability theory. Moving forward, exercises prompt learners to explore the intricacies of probability density functions, such as determining the density function of the square of a Gaussian random variable.

Furthermore, the exercises delve into the characteristics and properties of exponential random variables, including the examination of moments and the notable lack of memory property inherent to this distribution. Additionally, learners engage with advanced topics such as Chernoff’s bound, a powerful tool in probability theory for bounding tail probabilities of sums of independent random variables. Furthermore, exercises explore the application of characteristic functions and Laplace transforms in analyzing and interpreting continuous random variables.

6. Exercises on Random Vectors and Multivariate Gaussian Distributions

Exercises with Solutions: These exercises cover a range of topics including the independence of an event and a random variable, the characteristic function of a linear transformation of a random vector, and the covariance matrix of a linear combination of random vectors. Additional exercises explore the covariance matrix of a multinomial random vector, the independence and distribution of linear combinations of Gaussian variables, and scenarios where variables are Gaussian and uncorrelated but not jointly Gaussian. 

7. Exercises on Convergence of Sequences of Random Variables

Exercises with Solutions: These exercises cover a range of topics within the field of stochastic processes and probability theory. Titles such as « Infinite Visits to a Sit in a Stochastic Process » and « GI/GI/1 Queue Recurrence Relation Analysis » delve into complex behaviors in stochastic models. Others, like « Convergence in Probability vs. Almost-Sure Convergence » and « Convergence in Quadratic Mean of Series of Random Variables, » focus on different modes of convergence of random variables. Additionally, exercises like « Law of Rare Events – Poisson Limit Theorem » explore specific limit theorems in probability theory. 

8. Exercises on Stochastic Processes

Exercises with Solutions: The topics covered include « Constructing a Stochastic Process Using Kolmogorov’s Extension, » which explores the foundational aspects of stochastic process construction. « Harmonic Stochastic Process » and « Continuity of the Autocovariance Function of WSS Processes » delve into the analysis of process characteristics and their implications. The exercise on « Second-Order Properties in Frequency and Phase Modulated Signals » examines the impact of modulation on stochastic signals. « Stochastic Integral of a Right-Continuous Process » focuses on integration techniques within stochastic calculus. Lastly, « Properties of the Ornstein-Uhlenbeck Process » provides insight into this specific process known for its relevance in various scientific fields. 

9. Exercises on Conditional Expectation and MMSE Estimation

Exercises with Solutions: These exercises include: « Conditional Expectation with Joint PDF φ(y), » which explores the conditional expectation using a joint probability density function; « Conditional Expectation with Joint PDF kxy, » focusing on another joint PDF scenario; « Conditional Expectation of a Random Variable Given its Positive Part, » which examines the conditional expectation given the positive part of a random variable; and « Conditional Expectation of a Random Variable Given Its Square, » addressing the conditional expectation given the square of a random variable. These exercises are designed to deepen understanding through practical application of the lecture material.

Book on Measure Theory, Probability, and Stochastic Processes

Keep an eye on this page for the upcoming launch of our book:

  • B. Błaszczyszyn, L. Darlavoix, M.K. Karray: « Measure Theory, Probability, and Stochastic Processes: An Integrated Approach ».