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Results tagged with markov-chain-montecarlo
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user 48336
Markov Chain Monte Carlo (MCMC) refers to a class of simulation methods for generating samples from a complex target distribution by generating random numbers from a Markov Chain whose stationary distribution is the target distribution. MCMC methods are typically used when more direct methods for random number generation (e.g. inversion method) are infeasible. The very first MCMC method was the Metropolis (et al.) algorithm, later expanded by Hastings.
2
votes
Watanabe–Akaike/widely applicable information criterion (WAIC) using PyMC
You can use the following function:
def waic(trace, model=None):
"""
Calculate the widely available information criterion of the samples in trace from model.
"""
model = pm.modelc …
- 196
4
votes
Metropolis-Hastings acceptance rate confusion
The theory behinds Markov Chain Monte Carlo (the family of algorithms that includes the Metropolis algorithm as a special case) guarantees that (under certain conditions) Metropolis will give you the …
- 196