INTRODUCTION TO THE THEORY OF PROBABILITY

Name: INTRODUCTION TO THE THEORY OF PROBABILITY
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Rating: 3.7 (4019 reviews)

MATH/STAT 431

과목 설명

Topics covered include axioms of probability, random variables, the most important discrete and continuous probability distributions, expectation and variance, moment generating functions, conditional probability and conditional expectations, multivariate distributions, Markov's and Chebyshev's inequalities, laws of large numbers, and the central limit theorem.

선수과목

MATH 234 or MATH 376 or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) Program

충족 요건

ISYE 516 , STAT 312 , ISYE 412 , ISYE/MATH/OTM/STAT 632 , ACTSCI 650 , GENBUS 317 , ISYE/NE 574 , ISYE 615 , MATH 616 , MATH/STAT 309 , ISYE/PSYCH 349 , ISYE 320 , OTM 453 , ISYE 315 , ISYE/ME 512 , BMI/COMPSCI 567 , RMI 655 , ECON/FINANCE 300 , ISYE 517 , ISYE 521 , RMI 660 , STAT 311 , MEDPHYS 674 , ECON 400 , STAT 461 , COMPSCI 566 , CIVENGR 572 , CIVENGR 570 , CIVENGR 516 , COMPSCI/ECE 561 , ECE/ISYE 570 , COMPSCI 541 , MATH/STAT 310 , MATH 535 , ISYE 350 , and ISYE 624

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3.14

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95.28%

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36.22%

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127

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유사 과목

PROBABILITY THEORY

MATH 531

A rigorous introduction to probability theory at an advanced undergraduate level. Only a minimal amount of measure theory is used, in particular, the theory of Lebesgue integrals is not needed. It is aimed at math majors and Master's degree students, or students in other fields who will need probability in their future careers. Gives an introduction to the basics (Kolmogorov axioms, conditional probability and independence, random variables, expectation) and discusses some classical results with proofs (DeMoivre-Laplace limit theorems, the study of simple random walk on the one dimensional lattice, applications of generating functions).

MATHEMATICAL STATISTICS I

MATH/STAT 709

An introduction to measure theoretic probability, random variables, and high-dimensional statistics; conditional expectation, sufficiency, and unbiased estimation; methods of large sample theory including laws of large numbers and central limit theorems.

INTRODUCTORY PROBABILITY

MATH 331

Topics covered include axioms of probability, random variables, the most important discrete and continuous probability distributions, expectation and variance, conditional probability and conditional expectations, Markov's and Chebyshev's inequalities, laws of large numbers, and the central limit theorem. Includes a brief introduction to techniques of multivariate integration.

MATHEMATICAL STATISTICS I

STAT 609

Review of probability, random variables and vectors and their distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts for sequences of random variables, laws of large numbers, central limit and other limit theorems.

THEORY OF PROBABILITY I

MATH/STAT 733

An introduction to measure theoretic probability and stochastic processes.Topics include foundations, independence, zero-one laws, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, random walks, conditional expectations. Familiarity with basic measure theory (e.g.MATH 629or721) or concurrent registration inMATH 721is strongly recommended.