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INTRODUCTION TO STOCHASTIC PROCESSES

ISYE/MATH/OTM/STAT 632
과목 설명

Topics include discrete-time Markov chains, Poisson point processes, continuous-time Markov chains, and renewal processes. Applications to queueing, branching, and other models in science, engineering and business.

선수과목

(MATH/STAT 431 , MATH/STAT 309 , STAT 311 or MATH 531 ) and (MATH 320 , MATH 340 , MATH 341 , MATH 375 , MATH 421 or MATH 531 ) or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) Program

충족 요건
학점

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개설 시기

미보고

평점
3.25

6.35% 과거 데이터 대비

수료율
96.23%

3.71% 과거 데이터 대비

A 비율
32.08%

-13.12% 과거 데이터 대비

학급 규모
53

-7.56% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

SIMULATION AND PROBABILISTIC MODELING

ISYE 320

Analysis of stochastic systems using both analytic methods and computer simulation. Empirical and theoretical models of arrival and service processes. State spaces and state transition probabilities. Simulation of queuing and manufacturing systems. Continuous time Markov analysis of manufacturing systems. Simulation project management, testing and emerging trends.

PROBABILITY AND RANDOM PROCESSES

ECE 730

Review of basic probability. Advanced probability concepts. Random vectors; linear filtering of random processes; stationarity; power spectral densities; estimation; convergence; Markov chains; Poisson process; Wiener process. Knowledge of basic probability analysis [such asE C E 331,STAT/​MATH  431, orSTAT 311] strongly encouraged.

TOPICS IN THE THEORY OF PROBABILITY

MATH/STAT 833

Advanced topics in probability and stochastic processes.

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.

STOCHASTIC MODELING TECHNIQUES

ISYE 624

Techniques for modeling systems in which uncertainty is an essential factor. Emphasizes why, how and when techniques can or cannot be applied, rather than their mathematical derivation. Case studies and/or examples from such areas as logistics, production, and service industries.