Kernels#
Goose makes it easy for you to combine different MCMC kernels for different blocks of
model parameters. You can also define your own kernel by implementing
the Kernel protocol.
To draw samples from your posterior, you will want to call
sample_all_epochs. Once sampling is done, you can obtain the results
with get_results, which will return a SamplingResults
instance.
An IWLS kernel with dual averaging and an (optional) user-defined function for computing the Cholesky decomposition of the Fisher information matrix, implementing the |
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A NUTS kernel with dual averaging and an inverse mass matrix tuner, implementing the |
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A HMC kernel with dual averaging and an inverse mass matrix tuner, implementing the |
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A random walk kernel. |
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A Metropolis-Hastings kernel implementing the |
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Encapsulates a proposed state and the log-correction for a Metropolis-Hastings transition. |
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A Gibbs kernel implementing the |