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Stan
2.10.0
probability, sampling & optimization
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Markov chain Monte Carlo samplers. More...
Classes | |
class | adapt_dense_e_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and adaptive dense metric and adaptive step size. More... | |
class | adapt_dense_e_nuts_classic |
class | adapt_dense_e_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and adative dense metric and adaptive step size. More... | |
class | adapt_dense_e_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and adaptive dense metric and adaptive step size. More... | |
class | adapt_dense_e_xhmc |
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and adaptive dense metric and adaptive step size. More... | |
class | adapt_diag_e_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and adaptive diagonal metric and adaptive step size. More... | |
class | adapt_diag_e_nuts_classic |
class | adapt_diag_e_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and adaptive diagonal metric and adaptive step size. More... | |
class | adapt_diag_e_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and adaptive diagonal metric and adaptive step size. More... | |
class | adapt_diag_e_xhmc |
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and adaptive diagonal metric and adaptive step size. More... | |
class | adapt_softabs_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Riemannian disintegration and SoftAbs metric and adaptive step size. More... | |
class | adapt_softabs_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Riemannian disintegration and SoftAbs metric and adaptive step size. More... | |
class | adapt_softabs_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Riemannian disintegration and SoftAbs metric and adaptive step size. More... | |
class | adapt_softabs_xhmc |
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Riemannian disintegration and SoftAbs metric and adaptive step size. More... | |
class | adapt_unit_e_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and unit metric and adaptive step size. More... | |
class | adapt_unit_e_nuts_classic |
class | adapt_unit_e_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and unit metric and adaptive step size. More... | |
class | adapt_unit_e_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and unit metric and adaptive step size. More... | |
class | adapt_unit_e_xhmc |
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and unit metric and adaptive step size. More... | |
class | base_adaptation |
class | base_adapter |
class | base_hamiltonian |
class | base_hmc |
class | base_integrator |
class | base_leapfrog |
class | base_mcmc |
class | base_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling. More... | |
class | base_nuts_classic |
class | base_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time. More... | |
class | base_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time. More... | |
class | base_xhmc |
Exhaustive Hamiltonian Monte Carlo (XHMC) with multinomial sampling. More... | |
class | chains |
An mcmc::chains object stores parameter names and dimensionalities along with samples from multiple chains. More... | |
class | covar_adaptation |
class | dense_e_metric |
class | dense_e_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and dense metric. More... | |
class | dense_e_nuts_classic |
class | dense_e_point |
Point in a phase space with a base Euclidean manifold with dense metric. More... | |
class | dense_e_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and dense metric. More... | |
class | dense_e_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and dense metric. More... | |
class | dense_e_xhmc |
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and dense metric. More... | |
class | diag_e_metric |
class | diag_e_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and diagonal metric. More... | |
class | diag_e_nuts_classic |
class | diag_e_point |
Point in a phase space with a base Euclidean manifold with diagonal metric. More... | |
class | diag_e_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and diagonal metric. More... | |
class | diag_e_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and diagonal metric. More... | |
class | diag_e_xhmc |
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and diagonal metric. More... | |
class | expl_leapfrog |
class | fixed_param_sampler |
class | impl_leapfrog |
struct | nuts_util |
class | ps_point |
Point in a generic phase space. More... | |
class | sample |
struct | softabs_fun |
class | softabs_metric |
class | softabs_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Riemannian disintegration and SoftAbs metric. More... | |
class | softabs_point |
Point in a phase space with a base Riemannian manifold with SoftAbs metric. More... | |
class | softabs_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Riemannian disintegration and SoftAbs metric. More... | |
class | softabs_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Riemannian disintegration and SoftAbs metric. More... | |
class | softabs_xhmc |
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Riemannian disintegration and SoftAbs metric. More... | |
class | stepsize_adaptation |
class | stepsize_adapter |
class | stepsize_covar_adapter |
class | stepsize_var_adapter |
class | unit_e_metric |
class | unit_e_nuts |
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and unit metric. More... | |
class | unit_e_nuts_classic |
class | unit_e_point |
Point in a phase space with a base Euclidean manifold with unit metric. More... | |
class | unit_e_static_hmc |
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and unit metric. More... | |
class | unit_e_static_uniform |
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and unit metric. More... | |
class | unit_e_xhmc |
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and unit metric. More... | |
class | var_adaptation |
class | windowed_adaptation |
Functions | |
void | write_metric (stan::interface_callbacks::writer::base_writer &writer) |
void | stable_sum (double a1, double log_w1, double a2, double log_w2, double &sum_a, double &log_sum_w) |
a1 and a2 are running averages of the form ![]() ![]() ![]() ![]() | |
Markov chain Monte Carlo samplers.
void stan::mcmc::stable_sum | ( | double | a1, |
double | log_w1, | ||
double | a2, | ||
double | log_w2, | ||
double & | sum_a, | ||
double & | log_sum_w | ||
) |
a1 and a2 are running averages of the form
and the weights are the respective normalizing constants
This function returns the pooled average and the pooled weights
a1 | First running average, f1 / w1 |
log_w1 | Log of first summed weight |
a2 | Second running average |
log_w2 | Log of second summed weight |
sum_a | Average of input running averages |
log_sum_w | Log of summed input weights |
Definition at line 40 of file base_xhmc.hpp.
void stan::mcmc::write_metric | ( | stan::interface_callbacks::writer::base_writer & | writer | ) |
Definition at line 18 of file unit_e_point.hpp.