Samplers¶
pypbl.samplers¶
Samplers for Bayesian inference.

pypbl.samplers.
ensemble_sampler
(fun, start, sigma, iterations, verbose=False)¶ Sampler based on the affineinvariant ensemble sampler for Markov chain Monte Carlo
Parameters:  fun (function) – log probability function used to infer weights.
 start (list) – initial weights (it is recommended to use the MAP estimate).
 sigma (float) – term used to encourage different starting conditions for walkers.
 iterations (int) – number of iterations in sampling algorithm (total number of evaluation will be 2 * number of weights * iterations). # noqa
 verbose (boolean) – set as True to get verbose print out.
Returns: Numpy array of samples

pypbl.samplers.
simple_sampler
(fun, start, sigma, iterations, verbose=False)¶ Simple sampler based on the metropolis hastings algorithm for Markov chain Monte Carlo
Parameters:  fun (function) – log probability function used to infer weights
 start (list) – initial weights (it is recommended to use the MAP estimate)
 sigma (float) – diagonal term for proposal distribution covariance
 iterations (int) – number of iterations in sampling algorithm
 verbose (boolean) – set as True to get verbose print out
Returns: Numpy array of samples