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Chapter 5 Elementary Stochastic Analysis

Chapter 5 Elementary Stochastic Analysis

Chapter 5 Elementary Stochastic Analysis. Prof. Ali Movaghar. Goals in this chapter. Characterization of random processes Analysis of discrete and continuous time Markov chains Long term behavior of random processes Birth and death processes The methods of stages. Random Process.

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660 views • 53 slides
Chapter 5 Elementary Stochastic Analysis

Chapter 5 Elementary Stochastic Analysis

Chapter 5 Elementary Stochastic Analysis. Prof. Ali Movaghar. Goals in this chapter. Characterization of random processes Analysis of discrete and continuous time Markov chains Long term behavior of random processes Birth and death processes The methods of stages. Random Process.

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539 views • 53 slides
Lecture 7

Lecture 7

Lecture 7. Poisson Processes (a reminder) Some simple facts about Poisson processes The Birth/Death Processes in General Differential-Difference Equations The Pure Birth Process (Generating our own Poisson process from scratch) How are queues related to Markov chains The M/M/1 queue

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378 views • 18 slides
Lecture 7

Lecture 7

Lecture 7. Poisson Processes (a reminder) Some simple facts about Poisson processes The Birth/Death Processes in General Differential-Difference Equations The Pure Birth Process (Generating our own Poisson process from scratch) How are queues related to Markov chains The M/M/1 queue

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357 views • 18 slides
Continuous Time Markov Chains and Basic Queueing Theory

Continuous Time Markov Chains and Basic Queueing Theory

Continuous Time Markov Chains and Basic Queueing Theory. EE384X Review 4 Winter 2004. Review: DTMC. p ij is the transition probability from i to j over one time slot The time spent in a state is geometrically distributed Result of the Markov (memoryless) property

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294 views • 13 slides
If ? is not known then updating depends upon a distribution h(?)the hyperprior

If ? is not known then updating depends upon a distribution h(?)the hyperprior

The generalization of Bayes for continuous densities is that we have some density f( y | ? ) where y and ? are vectors of data and parameters with ? being sampled from a prior ?(?|?) where the ? are hyperparameters. If ? is known then Bayesian updating is.

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229 views • 13 slides

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