bayesbay.samplers.Sampler

class bayesbay.samplers.Sampler

Low-level class for defining the sampling criterion of a Markov chain and/or modifying its attributes in-between iterations

Reference Details

chains

the MarkovChain instances of the current Bayesian inference

parallel_config

customized parallel configuration dictionary that will be passed to joblib.Parallel

add_on_begin_iteration(func)

adds a custom function that will be called at the beginning of each Markov chain iteration (see on_begin_iteration())

add_on_end_advance_chain(func)

adds a custom function that will be called at the end of the batch of Markov chain iterations taking place after calling advance_chain()

add_on_end_iteration(func)

adds a custom function that will be called at the end of each Markov chain iteration (see on_end_iteration())

add_on_initialize(func)

adds a custom function that will be called at the end of the chains initialization (see initialize())

Parameters:

func (Callable) – Function that takes as arguments a Sampler and a list of MarkovChains

advance_chain(n_iterations, burnin_iterations=0, save_every=100, verbose=True, print_every=100)

advances the Markov chains for a given number of iterations

Parameters:

  • n_iterations (int) – the number of iterations to advance

  • burnin_iterations (int, optional) – the iteration number from which we start to save samples, by default 0

  • save_every (int, optional) – the frequency in which we save the samples, by default 100

  • verbose (bool, optional) – whether to print the progress during sampling or not, by default True

  • print_every (int, optional) – the frequency with which we print the progress and information during the sampling, by default 100 iterations

  • parallel_config (dict, optional) – keyword arguments passed to joblib.Parallel. Ignored when len(self.chains) is 1

Returns:

the Markov chains

Return type:

List[BaseMarkovChain]

begin_iteration(chain)

calls on_begin_iteration() before beginning the current Markov chain iteration

Parameters:

chain (bayesbay.BaseMarkovChain) – Markov chain used to sample the posterior

end_advance_chain()

calls on_end_advance_chain() before concluding the batch of Markov chain iterations taking place after calling advance_chain()

end_iteration(chain)

calls on_end_iteration() before passing to the next Markov chain iteration.

Parameters:

chain (bayesbay.BaseMarkovChain) – Markov chain used to sample the posterior

initialize(chains)

initializes the given Markov Chains by calling on_initialize()

Parameters:

chains (List[BaseMarkovChain]) – List of Markov chains used to sample the posterior

abstractmethod on_begin_iteration(chain)

defines the behaviour of the sampler at the beginning of each Markov chain iteration

Parameters:

chain (BaseMarkovChain) – Markov chain used to sample the posterior

abstractmethod on_end_advance_chain()

defines the behaviour of the sampler at the end of the batch of Markov chain iterations taking place after calling advance_chain()

Parameters:

chain (BaseMarkovChain) – Markov chain used to sample the posterior

abstractmethod on_end_iteration(chain)

defines the behaviour of the sampler at the end of each Markov chain iteration

Parameters:

chain (BaseMarkovChain) – Markov chain used to sample the posterior

abstractmethod on_initialize(chains)

defines the behaviour of the sampler at the initialization of the Markov chains

Parameters:

chains (List[BaseMarkovChain]) – List of Markov chains used to sample the posterior

abstractmethod run()

function that allows for running the Bayesian inference by calling advance_chain().

Important

To work properly, a custom Sampler has to call within this function the method advance_chain().

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