Samplers#
This submodule contains functions for MCMC and forward sampling.
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Draw samples from the posterior using the given step methods. |
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Generate samples from the prior predictive distribution. |
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Generate posterior predictive samples from a model given a trace. |
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Generate weighted posterior predictive samples from a list of models and a list of traces according to a set of weights. |
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Draw samples from the posterior using the NUTS method from the |
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Draw samples from the posterior using the NUTS method from the |
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Set up the mass matrix initialization for NUTS. |
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Draw samples for one variable or a list of variables |
Step methods#
HMC family#
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A sampler for continuous variables based on Hamiltonian mechanics. |
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A sampler for continuous variables based on Hamiltonian mechanics. |
Metropolis family#
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A Metropolis-within-Gibbs step method optimized for binary variables |
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Metropolis-Hastings optimized for binary variables |
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A Metropolis-within-Gibbs step method optimized for categorical variables. |
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Differential Evolution Metropolis sampling step. |
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Adaptive Differential Evolution Metropolis sampling step that uses the past to inform jumps. |
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Metropolis-Hastings sampling step |
Other step methods#
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Step method composed of a list of several other step methods applied in sequence. |
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Univariate slice sampler step method. |