Defines the interface to an automated factor slice sampler, which isn't as efficient as Stan in terms of ESS per iteration, but doesn't require gradients and tends to do better than an adaptive MH algorithm. Additionally it hardly uses any tuning parameters.

#include "sampling/sampler_automated_factor_slice_sampler_MCMC.h"

Inheritance diagram for sampler_automated_factor_slice_sampler_MCMC:

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Collaboration diagram for sampler_automated_factor_slice_sampler_MCMC:

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Public Member Functions
	sampler_automated_factor_slice_sampler_MCMC (int seed, likelihood &L, std::vector< std::string > var_names, size_t dimension)

void	set_initial_location (std::vector< double > initial)
	Sets the starting location for the sampler. More...

void	run_sampler (int nsteps, int thin, int refresh, int verbosity=1)

void	set_adaptation_parameters (int num_warmup, bool save_adapt)
	sets the adaption interval for the slice sampler. This will adapt the directions of the slices and the windowing procedure for the problem. This is important for performance. More...

void	set_intial_widths (std::vector< double > intitial_width)
	Sets the initial widths to be used for slices. More...

void	set_window_parameters (int init_buffer, int window)
	Sets the window parameters for the adaptation. This is inspired by stan. The initial window will be solely for building the initial covariance estimate and finding some reasonable initial widths for the slices. Window then says how long the initial covariance adaptation round will last. We then increase by factors of 2 until we have exhausted the adaptation stage of the sampler. More...

void	set_initial_covariance (Eigen::MatrixXd covariance)
	Sets the initial covariance matrix for the slice sampler. This will then be diagonalized to find the principal directions. More...

void	get_chain_state (std::vector< double > &parameters, std::vector< double > &sample_state)

void	set_chain_state (std::vector< double > model_values, std::vector< double > state_values)

void	write_checkpoint (std::ostream &)

void	read_checkpoint (std::string ckpt_file)
	Restarts the sampler from the state specified in the ckpt_file. More...

void	read_checkpoint (std::istream &ckpt_in)

void	reset_sampler_step ()

Public Member Functions inherited from sampler_MCMC_base
	sampler_MCMC_base (int seed, likelihood &L, std::vector< std::string > var_names, size_t dimension)

virtual void	set_output_stream (std::string chain_name, std::string state_name, std::string sampler_name, int output_precision=8)
	Sets the output names for the chains and sampler states. This must be implemented in any classes inheriting sampler_exploration_mcmc_base. More...

virtual std::vector< double >	find_best_fit (std::string chain_file, std::string state_file) const
	Finds the best fit within provided chain file and returns it. More...

virtual std::vector< double >	find_best_fit () const
	Finds the best fit from the chain and state file saved in set_output_stream. More...

void	set_checkpoint (int ckpt_stride, std::string ckpt_file)
	Sets the checkpoint/restart functionality. More...

void	reset_likelihood_sum ()

double	get_likelihood_sum ()

virtual double	get_rand ()

virtual void	reset_rng_seed (int seed)

void	set_mpi_communicator (MPI_Comm comm)
	Sets the MPI_Comm to be used for the likelihood evaluation. More...

void	close_streams ()
	Helper function that closes the streams from a sampler.

Private Member Functions
void	write_state ()

void	write_chain_header ()

void	write_state_header ()

void	write_sampler_header ()

double	loglklhd_transform (std::vector< double > cont_params)

void	generate_transition (int step, int start, int finish, int nthin, int refresh, bool save, int verbosity)

void	factor_slice_sample (const int k, const double h)

void	ray_position (int k, double t, std::vector< double > &ray)

int	step_out (const int k, double logh, std::vector< double > &lower_ray, std::vector< double > &upper_ray, double &tmin, double &tmax)

void	shrink (int k, std::vector< double > &lower_ray, std::vector< double > &upper_ray, double t)

void	update_covariance (std::vector< double > sample)

void	set_adaptation_schedule ()

double	factor_adapt ()

void	width_adapt ()

Private Attributes
std::vector< double >	_current_parameters

std::vector< double >	_current_state

bool	_adaptation

bool	_save_adaptation

int	_nadapt

int	_start_step

double	_width_tol

double	_cov_tol

int	_width_adapt_stride

int	_cov_adapt_stride

int	_adapt_width_count

int	_adapt_cov_count

int	_number_slow_adapt
	restart flag.

bool	_restart

Eigen::MatrixXd	_Gammak

Eigen::MatrixXd	_covariance

Eigen::VectorXd	_Dk

Eigen::VectorXd	_mean

std::vector< double >	_initial_width

std::vector< int >	_nexpansion

std::vector< int >	_ncontraction

std::vector< bool >	_width_adapt

int	_init_buffer

int	_window

std::vector< double >	_lbound

std::vector< double >	_ubound

std::vector< Themis::transform_base * >	_transforms

Additional Inherited Members
Protected Attributes inherited from sampler_MCMC_base
int	_seed

Ran2RNG	_rng

std::vector< std::string >	_var_names

size_t	_dimension

size_t	_step_count

likelihood *	_L

std::string	_chain_file

std::string	_state_file

std::string	_sampler_file

std::string	_ckpt_file

std::ofstream	_chain_out

std::ofstream	_state_out

std::ofstream	_sampler_out

int	_output_precision

int	_ckpt_stride

double	_sum_lklhd

MPI_Comm	_comm

int	_rank

Member Function Documentation

void read_checkpoint ( std::string ckpt_file )

inlinevirtual

Parameters

ckpt_file The file where the checkpoint information was saved.

Reimplemented from sampler_MCMC_base.

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void set_adaptation_parameters	(	int	num_warmup,
		bool	save_adapt
	)

Parameters

num_warmup	The number of adaptation steps to take. Default is 10,000.
save_adapt	Whether to save the adaptation steps.

Note: Be aggressive with adaptation.

void set_initial_covariance ( Eigen::MatrixXd covariance )

Parameters

covariance. The covariance matrix for the problem.

void set_initial_location ( std::vector< double > initial )

virtual

Parameters

initial_parameters The initial location of the sampler. This must be set before calling run_sampler.

Implements sampler_MCMC_base.

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void set_intial_widths ( std::vector< double > intitial_width )

Parameters

intitial_width The initial widths of the slices to be used.

Note: If you aren't sure what these should be make them too big. It is cheaper to shrink a slice than to expand it.

void set_window_parameters	(	int	init_buffer,
		int	window
	)

Note: at the the end of this we will have nadapt steps.

Parameters

init_buffer	The initial length to adapt the slices.
window	The initial length of the covariance adaptation.

The documentation for this class was generated from the following files:

sampling/sampler_automated_factor_slice_sampler_MCMC.h
sampling/sampler_automated_factor_slice_sampler_MCMC.cpp

Public Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Member Function Documentation