On Mixing Time for Some Markov Chain Monte Carlo, M.Sc. Thesis Sharif University of Technology ; Alishahi, Kasra (Supervisor)
Abstract
Markov chains are memoryless stochastic processes that undergoes transitions from one state to another state on a state space having the property that, given the present,the future is conditionally independent of the past. Under general conditions, the markov chain has a stationary distribution and the probability distribution of the markov chain, independent of the staring state, converges to it’s stationary distribution.
We use this fact to construct markov chain monte carlo, which are a class of algorithms for sampling from probability distributions based on constructing a markov chain that has the desired distribution as its stationary distribution. The state of a chain after a large... Cataloging briefOn Mixing Time for Some Markov Chain Monte Carlo, M.Sc. Thesis Sharif University of Technology ; Alishahi, Kasra (Supervisor)
Abstract
Markov chains are memoryless stochastic processes that undergoes transitions from one state to another state on a state space having the property that, given the present,the future is conditionally independent of the past. Under general conditions, the markov chain has a stationary distribution and the probability distribution of the markov chain, independent of the staring state, converges to it’s stationary distribution.
We use this fact to construct markov chain monte carlo, which are a class of algorithms for sampling from probability distributions based on constructing a markov chain that has the desired distribution as its stationary distribution. The state of a chain after a large... Find in contentBookmark |
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