tfp.experimental.sts_gibbs.fit_with_gibbs_sampling

Fits parameters for an STS model using Gibbs sampling.

tfp.experimental.sts_gibbs.fit_with_gibbs_sampling(
    model,
    observed_time_series,
    num_chains=(),
    num_results=2000,
    num_warmup_steps=200,
    initial_state=None,
    seed=None,
    default_pseudo_observations=None,
    experimental_use_dynamic_cholesky=False,
    experimental_use_weight_adjustment=False
)

model A tfp.sts.StructuralTimeSeries model instance return by build_model_for_gibbs_fitting.
observed_time_series float Tensor of shape [..., T, 1](omitting the trailing unit dimension is also supported whenT > 1), specifying an observed time series. May optionally be an instance of <a href="../../../tfp/sts/MaskedTimeSeries"><code>tfp.sts.MaskedTimeSeries</code></a>, which includes a maskTensorto specify timesteps with missing observations. </td> </tr><tr> <td>num_chains<a id="num_chains"></a> </td> <td> Optional int to indicate the number of parallel MCMC chains. Default to an empty tuple to sample a single chain. </td> </tr><tr> <td>num_results<a id="num_results"></a> </td> <td> Optional int to indicate number of MCMC samples. </td> </tr><tr> <td>num_warmup_steps<a id="num_warmup_steps"></a> </td> <td> Optional int to indicate number of MCMC samples. </td> </tr><tr> <td>initial_state<a id="initial_state"></a> </td> <td> AGibbsSamplerStatestructure of the initial states of the MCMC chains. </td> </tr><tr> <td>seed<a id="seed"></a> </td> <td> OptionalPythonintseed controlling the sampled values. </td> </tr><tr> <td>default_pseudo_observations<a id="default_pseudo_observations"></a> </td> <td> Optional scalar floatTensorControls the number of pseudo-observations for the prior precision matrix over the weights. </td> </tr><tr> <td>experimental_use_dynamic_cholesky<a id="experimental_use_dynamic_cholesky"></a> </td> <td> Optional bool - in case of spike and slab sampling, will dynamically select the subset of the design matrix with active features to perform the Cholesky decomposition. This may provide a speedup when the number of true features is small compared to the size of the design matrix. *Note*: If this is true, neither batch shape norjit_compileis supported. </td> </tr><tr> <td>experimental_use_weight_adjustment` Optional bool - use a nonstandard update for the posterior precision of the weight in case of a spike and slab sampler.

model A GibbsSamplerState structure of posterior samples.