# Advanced topics MCMC 2022

### From Werner KRAUTH

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Here is the program [[TVDTemperingLift.py| TVDTemperingLift.py]] which computes the probability distribution pi^t and the TVD with the stationary probability distribution. | Here is the program [[TVDTemperingLift.py| TVDTemperingLift.py]] which computes the probability distribution pi^t and the TVD with the stationary probability distribution. | ||

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+ | [http://www.lps.ens.fr/%7Ekrauth/images/8/83/CM_2022_9.1.pdf Here is the CM9.1] of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo. | ||

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+ | [http://www.lps.ens.fr/%7Ekrauth/images/a/ae/TD09_ICFP_ADV_MCMC.pdf Here is the TD9] |

## Current revision

This is the homepage of my 2022 lecture course on Advanced topics in Markov-chain Monte Carlo

Here is the CM1.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.

Here is the CM2.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.

Here is the CM3.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.

Here is the CM4.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.

Here is the CM5.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.

Here is the CM6.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.

Here is the CM7.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.

Here is the CM8.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.

Here is the program TemperingConductance.py which iterates over the powerset of Omega, in order to compute the conductance of the Simulated Tempering problem.

Here is the program TVDTemperingLift.py which computes the probability distribution pi^t and the TVD with the stationary probability distribution.

Here is the CM9.1 of my 2022 lecture course Advanced topics in Markov-chain Monte Carlo.