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source: Bilkent Online Courses 2014年8月17日
IE-325 Stochastic Models by Savaş Dayanık
Probability review and conditional expectations. Discrete-time Markov chains. Markov decision processes. Poisson processes. Continuous-time Markov chains. Applications to inventory control, queuing systems, and cash management.
IE-325 Stochastic Models Lecture 01 Poisson Processes cont'd 54:56
Lecture 02 Probability Review 1:03:24
Lecture 03 Conditional Problems 46:32
Lecture 04 Random Variables, Cumulative Distribution Function 1:02:18
Lecture 05 Random variable, Indicator random variables 45:23
Lecture 06 Examples 53:53
Lecture 07 Discrete distributions, Law of rare events 49:13
Lecture 08 1:01:15
Lecture 09 Examples 39:27
Lecture 10 Introduction to Markov Chains 48:03
Lecture 11 Introduction to Markov Chains 53:56
Lecture 12 N-step transition probabilities 1:00:44
Lecture 13 First-step Analysis 52:48
Lecture 14 First-step Analysis 56:41
Lecture 15 Random walk Gambler's ruin problem 40:12
Lecture 16 Gambler's Ruin (cont'd), Age-replacement, Intro to long-run behavior of Markov Chains 57:08
Lecture 17 Long-run behavior of Markov Chains 40:06
Lecture 18 Stationary Distributions 58:48
Lecture 19 Examples 57:12
Lecture 20 Long-run behavior of Markov Chains (cont'd) 42:42
Lecture 21 Examples 48:23
Lecture 22 Age Replacement Policies 49:54
Lecture 23 Markov Decision Processes 50:36
Lecture 24 Examples and Formulation 58:41
Lecture 25 Linear Programming Formulation 55:53
Lecture 26 Markov Decision Processes, Policy Improvement Algorithm 44:05
Lecture 27 Markov Decision Processes, Policy Improvement Algorithm (cont'd) 50:30
Lecture 28 Solution of Optimal Maintenance problem using Policy Iteration Algorithm 48:30
Lecture 29 Poisson Processes 58:51
Lecture 30 Examples with Poisson Processes 47:33
Lecture 31 Examples with Poisson Processes (cont'd) 47:10
Lecture 32 Distributions Related with Poisson Processes 58:03
Lecture 33 Uniform Distribution and Poisson Distribution 43:02
Lecture 34 Examples 30:54
Lecture 35 Continious-time Markov Chains Introduction 45:34
Lecture 36 Continious-time Markov Chains Introduction (cont'd) 56:03
Lecture 37 Kolmogorov's backward/forward equations 56:43
Lecture 38 Limiting Probabilities 49:54
Lecture 39 Limiting Probabilities (cont'd) 58:49
Lecture 40 Examples 25:05
