2019 Probability Theory

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Academic unit or major
Undergraduate major in Mathematics
Instructor(s)
Ninomiya Syoiti 
Course component(s)
Lecture
Day/Period(Room No.)
Wed3-4(H103)  Fri7-8(H103)  
Group
-
Course number
MTH.C361
Credits
2
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/3/18
Lecture notes updated
2019/9/13
Language used
Japanese
Access Index

Course description and aims

In this course, we introduce fundamental concepts in measure-theoretic probability theory, and we study basic limit theorems by means of those concepts. We first define several basic concepts which form a basis of the whole probability theory, and study their elementary properties. More precisely, we introduce probability space, probability measure, random variables, probability distribution, expectation and independence. On the basis of these preparations, we formulate and prove the law of large numbers and the central limit theorem, which are ones of most fundamental limit theorems.
Kolmogorov's axiomization of probability theory by means of measure theory provide a rigorous mathematical basis to the concept of probability, while it had been broadly used in the literature even before. In particular, this "revolution" makes it possible to develop arguments involving infinity precisely and we can state several limit theorems mean without ambiguity. Through this course, we will reveal how we justify concepts, theorems and computations, which were treated intuitively, and what properties they enjoy.

Student learning outcomes

Students are expected to:
Be able to follow arguments of measure-theoretic probability theory.
Be able to compute characteristics (expectation, variance and characteristic function etc.) of elementary distributions.
Understand the definition and properties of convergences of random variables and distributions, and be able to explain elementary examples.
Be able to explain how we formulate the law of large numbers and the central limit theorem rigorously.
Be able to explain an outline of the proof of these limit theorems.

Keywords

Probability space, probability measure, random variable, probability distribution, expectation, independence, almost-sure convergence, convergence in probability, Borel-Cantelli's lemma, law of large numbers, convergence in law, characteristic function, central limit theorem,martingale

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
- - - -

Class flow

Standard lecture course.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Probability space, probability measure, Borel-Cantelli theorem Details will be provided during each class session
Class 2 Random variables, Independence Details will be provided during each class session
Class 3 Kolmogorov's 0-1 law Details will be provided during each class session
Class 4 Expectation Details will be provided during each class session
Class 5 Conditional Expectations Details will be provided during each class session
Class 6 Discrete Time Martingale Details will be provided during each class session
Class 7 Optional Stopping Theorem, Martingale Convergence Theorem Details will be provided during each class session
Class 8 strong law of large numbers Details will be provided during each class session
Class 9 Characteristic Functions Details will be provided during each class session
Class 10 Applications of strong law of large numbes, convergence of probability measures Details will be provided during each class session
Class 11 Weak Convergence Details will be provided during each class session
Class 12 Basic properties of characteristic functions, examples of characteristic functions Details will be provided during each class session
Class 13 Characteristic functions and distributions Details will be provided during each class session
Class 14 Central limit theorem Details will be provided during each class session
Class 15 Some additional topics Details will be provided during each class session

Textbook(s)

None required.

Reference books, course materials, etc.

David Williams, ``Probability with Martingales'', Cambridge University Press

Assessment criteria and methods

Final exam (about 50%) and report (about 50%).

Related courses

  • MTH.C211 : Applied Analysis I
  • MTH.C212 : Applied Analysis II
  • MTH.C305 : Real Analysis I
  • MTH.C306 : Real Analysis II

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students are expected to have passed Applied Analysis I, Applied Analysis II, Real Analysis I and Real Analysis II.

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