Markov-Chain Monte-Carlo

class fitr.inference.mcmc.MCMC(generative_model=None, name='FitrMCMCModel')

Uses Markov-Chain Monte-Carlo (via PyStan) to estimate models

Attributes:

name : str

Name of the model being fit

generative_model : GenerativeModel object

Methods

fit(self, data, chains=4, n_iterations=2000, warmup=None, thin=1, seed=None, init=’random’, sample_file=None, algorithm=’NUTS’, control=None, n_jobs=-1, compile_verbose=False, sampling_verbose=False)	Runs the MCMC Inference procedure with Stan
__initresults(self)	(Private) method to initialize MCMCFitResult object

fit(data, chains=4, n_iterations=2000, warmup=None, thin=1, seed=None, init='random', sample_file=None, algorithm='NUTS', control=None, n_jobs=-1, compile_verbose=False, sampling_verbose=False)

Runs the MCMC Inference procedure with Stan

Parameters:

data : dict

Subject level data

chains : int > 0

Number of chains in sampler

n_iter : int

How many iterations each chain should run (includes warmup)

warmup : int > 0, iter//2 by default

Number of warmup iterations.

thin : int > 0

Period for saving samples

seed : int or np.random.RandomState, optional

Positive integer to initialize random number generation

sample_file : str

File name specifying where samples for all parameters and other saved quantities will be written. If None, no samples will be written

algorithm : {‘NUTS’, ‘HMC’, ‘Fixed_param’}, optional

Which of Stan’s algorithms to implement

control : dict, optional

Dictionary of parameters to control sampler’s behaviour (see PyStan documentation for details)

n_jobs : int, optional

Sample in parallel. If -1, all CPU cores are used. If 1, no parallel computing is used

compile_verbose : bool

Whether to print output from model compilation

sampling_verbose : bool

Whether to print intermediate output from model sampling

Returns:

ModelFitResult

Instance containing model fitting results

References

[1]	PyStan API documentation (https://pystan.readthedocs.io)