GPy.inference.mcmc package

Submodules

GPy.inference.mcmc.hmc module

class HMC(model, M=None, stepsize=0.1)[source]

An implementation of Hybrid Monte Carlo (HMC) for GPy models

Initialize an object for HMC sampling. Note that the status of the model (model parameters) will be changed during sampling.

Parameters:
  • model (GPy.core.Model) – the GPy model that will be sampled
  • M (numpy.ndarray) – the mass matrix (an identity matrix by default)
  • stepsize (float) – the step size for HMC sampling
sample(num_samples=1000, hmc_iters=20)[source]

Sample the (unfixed) model parameters.

Parameters:
  • num_samples (int) – the number of samples to draw (1000 by default)
  • hmc_iters (int) – the number of leap-frog iterations (20 by default)
Returns:

the list of parameters samples with the size N x P (N - the number of samples, P - the number of parameters to sample)

Return type:

numpy.ndarray

class HMC_shortcut(model, M=None, stepsize_range=[1e-06, 0.1], groupsize=5, Hstd_th=[1e-05, 3.0])[source]
sample(m_iters=1000, hmc_iters=20)[source]

GPy.inference.mcmc.samplers module

class Metropolis_Hastings(model, cov=None)[source]

Bases: object

new_chain(start=None)[source]
predict(function, args)[source]

Make a prediction for the function, to which we will pass the additional arguments

sample(Ntotal=10000, Nburn=1000, Nthin=10, tune=True, tune_throughout=False, tune_interval=400)[source]

Module contents