Tools of Modern Probability -- fall semester 2024


Subject code: BMETE95AM33

Classes:
Lecturer: Imre Péter Tóth

QUESTIONS AND ANSWERS sessions before the exams, in person (not online):
HOMEWORK RESULTS. Each homework exercise is evaluated with a CODE 0,1,2 or 3 (which is not a score itself). Meaning of the code: These codes are converted to homework scores using the following rule: So the maximum score is 12, which is then scaled up to 40 (meaning multiplication by 40/12 to obtain a homework score on the scale 0-40. Out of these, 20 are needed to obtain the signature for the course.

EXAMS: The exam will be ORAL. Please register for the exam in Neptun. When the registration closes (at noon before the day of the exam), I will decide on the order of the registered students, and let you know when it will be your turn. This may be up to 4 hours after the start of the exam. If your time is limited, please let me know, so that I can take it into account. Also: I'm flexible about exam times. If the exams announced in Neptun are not good for you, contact me, and we try to find a suitable time.

On the exam, every student gets at least one calculation exercise (similar to those in the exercise sheets), and two randomly chosen theoretical questions from the following list. NOTE: Before the exam, everybody can choose two of these questions, which she/he would not like, to exclude from the list.
Note: The material for exercises 10.1-10.10 was covered only in big steps, so don't worry about these.
  1. Gaussian integrals; spherically symmetric integrals, surface of hyperspheres [notes/01, section 1-2]
  2. Almost Gaussian integrals, Laplace's method [notes/01, section 3]
  3. Euler gamma function, Stirling's approximation [notes/01, section 4-5]
  4. Measure space, probability space. Push-forward of measures. Distribution of random variables [notes/02, section 1.1, 1.2; D, section 1.1-1.5]
  5. Integral, expectation. Integration by substitution. Expectation of random variables. Densities of measures. Sums of series and Riemannian integrals as special cases of the (Lebesgue) integral. [notes/02, section 1.3.1; D, section 1.6]
  6. Construction of random variables with a given distribution, generalized inverse of distribution functions [D, Theorem 1.2.2]
  7. Exchanging the integral and the limit: monotone convergence theorem, dominated convergence theorem, Fatou lemma. [notes/02, section 1.3.2; D, section 1.6.2]
  8. Characteristic functions of random variables, characteristic functions of probability distributions. Continuity, differentiability, continuous differentiability of the characteristic function. [Exercises 2.10,...,2.14]
  9. Product space, product measure. Exchanging integrals: Fubini's theorem [D, section 1.7]
  10. Weak convergence of random variables, weak convergence of probability distributions. Equivalent descriptions, relation to strong convergence. [D, Theorem 3.2.2, Theorem 3.2.3]
  11. Subsequential limits for distribution functions, vague limits, tightness. [D, Theorem 3.2.6, Theorem 3.2.7]
  12. Hilbert spaces - completeness of L^2 [notes/10,11,12]
  13. Riesz representation theorem [notes/13]
  14. Absolute continuity. Radon-Nikodym theorem. [notes/14]
  15. Conditional expectation of random variables. Definition, existence, uniqueness. Calculation in simple examples. [notes/15, Exercises 10.11-10,17]

HOME WORK
Practice exercises Here.

Course organization:
Grading rules:
HOMEWORK SUBMISSION: Home works can be submitted either on paper, or electronically via the Moodle system in pdf. If you submit online, please make sure that the file you upload is a single pdf, well readable and of moderate size. (The limit is 10 MB, but 1 MB is enough for everything.)

Preliminary plan of topics (for sure, not all will be covered): Videos of lectures from 2020 that were live:
These videos are not cut at the natural boundaries of the material presented, but at the brakes of the classes where they were recorded. So their descriptions only describe the content roughly.
Lecture 1, part 1: Gaussian integrals (46:21, 357 MB)
Lecture 1, part 2: Spherically symmetric integrals (32:57, 263 MB)
Lecture 2, part 1: Surface of hyperspheres (45:19, 301 MB)
Lecture 2, part 2: Euler Gamma function (32:41, 134 MB)
Lecture 3, part 1: Notation for asymptotic behaviour of functions SORRY, no video: I used the wrong settings, and the quality is unacceptable.
Lecture 3, part 2: Almost Gaussian integrals 1 (49:15, 226 MB)
Lecture 4, part 1: Almost Gaussian integrals 2 (35:14, 134 MB)
Lecture 4, part 2: liminf/limsup and Almost Gaussian integrals 3 (45:14, 200 MB)
Question session (no official class on sports day): Integration of spherically symmetric functions repeated (1:02:00, 323 MB)
Lecture 5, part 1: Examples of almost Gaussian integrals (52:40, 368 MB)
Lecture 5, part 2: Normalizing almost Gaussian functions to Gaussian; Stirling's approximation (27:18, 164 MB)
Lecture 6, part 1: Beta function (36:40, 138 MB)
Lecture 6, part 2: Gamma and beta distributions in Statistical Physics (43:34, 164 MB)
Lecture 7, part 1: Measure theory basics 1 (44:00, 145 MB)
Lecture 7, part 2: Measure theory basics 2 (36:36, 117 MB)
Lecture 8, part 1: Sorry, no video - I managed to record the break instead.
Lecture 8, part 2: Distributions of random variables (33:33, 92 MB)
Lecture 9, part 1: Remarks on homework solutions (20:44, 62 MB)
Lecture 9, part 2: The notion of Lebesgue integral (1:00:40, 201 MB)
Lecture 10, part 1: Expectation of random variables (46:05, 166 MB)
Lecture 10, part 2: Density of measures and calculating integrals (32:59, 114 MB)
Lecture 11, part 1: Integral substitution theorem, expectation of distributions (43:17, 145 MB)
Lecture 11, part 2: Properties of the integral and expectation, with examples (38:26, 125 MB)
Lecture 12, part 1: Description and construction of random variables (51:37, 147 MB)
Lecture 12, part 2: Generalized inverse of distribution funcitons, remark about the Zikkurat method (31:35, 81 MB)
Lecture 13, part 1: Types of convergence of functions and random variables (42:58, 135 MB)
Lecture 13, part 2: Convergence of functions vs convergence of integrals (36:09, 107 MB)
Lecture 14, part 1: Exchanging limit and integral: Monotone concvergence thm, Dominated convergence thm, Fatou's lemma (53:29, 153 MB)
Lecture 14, part 2: Example of dominated convergence: continuity and differentiability of characteristic functions (31:29, 85 MB)
Lecture 15, part 1: Differentiability of the characteristic function continued (38:39, 130 MB)
Lecture 15, part 1: Exchanging integrals: Fubini's theorem (34:49, 119 MB)
Lecture 16, part 1: Weak convergence of random variables, distributions and distribution functions (41:06, 158 MB)
Lecture 16, part 2: Equivalence of notions of weak convergence; relation to strong convergence (35:00, 128 MB)
Lecture 17, part 1: Weak convergence on metric spaces, Skorokhod representation theorem (33:28, 113 MB)
Lecture 17, part 2: Subsequential limits for distribution functions, vague limits, tightness (52:20, 180 MB)
Lecture 18, part 1: Weak convergence and tightness (27:34, 108 MB)
Lecture 18, part 2: Why we like limit theorems, weak convergence and characteristic functions (38:24, 152 MB)

Material that was pre-recorded (not live): Suggested literature