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License: MIT License
Implementation of Markov Chain Monte Carlo in Python from scratch
License: MIT License
acceptance
call should be refactored to acceptance_rule
in metropolis_hastings
Bug initially posted by gc-ms-training on [gist]( gist https://gist.github.com/Joseph94m/00f102e1e838433c1ec21536b3bea003)
The official design of the metropolis hastings algorithm is to always accept a sample.
accepted.append(x)
should be added in the else
in the following code.
x = param_init
accepted = []
rejected = []
for i in range(iterations):
x_new = transition_model(x)
x_lik = likelihood_computer(x,data)
x_new_lik = likelihood_computer(x_new,data)
if (acceptance_rule(x_lik + np.log(prior(x)),x_new_lik+np.log(prior(x_new)))):
x = x_new
accepted.append(x_new)
else:
>>>>accepted.append(x)<<<<
rejected.append(x_new)
return np.array(accepted), np.array(rejected)```
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