Metropolis Algorithm: Example Plots

New York University

- An  MCMC algorithm 
- Goal: to draw samples from an unknown target distribution 
- Uses a random walk approach through the distribution space, and it accepts or rejects a sample based on the likelihood of the proposed sample. 
- The likelihood is commonly decided by a probability density function 

Limitations: 
- The proposal distribution must be symmetric (equal prob of proposing a move from A to B, as from B to A)
- Metropolis Hastings Algorithm - A newer version -  resolves this limitation and allows fro asymmetric distributions. 

Metropolis Algorithm

McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd ed.). Chapman and Hall/CRC.

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