View source: R/sts-functions.R
sts_fit_with_hmc | R Documentation |
Markov chain Monte Carlo (MCMC) methods are considered the gold standard of Bayesian inference; under suitable conditions and in the limit of infinitely many draws they generate samples from the true posterior distribution. HMC (Neal, 2011) uses gradients of the model's log-density function to propose samples, allowing it to exploit posterior geometry. However, it is computationally more expensive than variational inference and relatively sensitive to tuning.
sts_fit_with_hmc( observed_time_series, model, num_results = 100, num_warmup_steps = 50, num_leapfrog_steps = 15, initial_state = NULL, initial_step_size = NULL, chain_batch_shape = list(), num_variational_steps = 150, variational_optimizer = NULL, variational_sample_size = 5, seed = NULL, name = NULL )
observed_time_series |
|
model |
An instance of |
num_results |
Integer number of Markov chain draws. Default value: |
num_warmup_steps |
Integer number of steps to take before starting to
collect results. The warmup steps are also used to adapt the step size
towards a target acceptance rate of 0.75. Default value: |
num_leapfrog_steps |
Integer number of steps to run the leapfrog integrator
for. Total progress per HMC step is roughly proportional to |
initial_state |
Optional Python |
initial_step_size |
|
chain_batch_shape |
Batch shape ( |
num_variational_steps |
|
variational_optimizer |
Optional |
variational_sample_size |
integer number of Monte Carlo samples to use
in estimating the variational divergence. Larger values may stabilize
the optimization, but at higher cost per step in time and memory.
Default value: |
seed |
integer to seed the random number generator. |
name |
name prefixed to ops created by this function. Default value: |
This method attempts to provide a sensible default approach for fitting StructuralTimeSeries models using HMC. It first runs variational inference as a fast posterior approximation, and initializes the HMC sampler from the variational posterior, using the posterior standard deviations to set per-variable step sizes (equivalently, a diagonal mass matrix). During the warmup phase, it adapts the step size to target an acceptance rate of 0.75, which is thought to be in the desirable range for optimal mixing (Betancourt et al., 2014).
list of:
samples: list
of Tensors
representing posterior samples of model
parameters, with shapes [concat([[num_results], chain_batch_shape, param.prior.batch_shape, param.prior.event_shape]) for param in model.parameters]
.
kernel_results: A (possibly nested) list
of Tensor
s representing
internal calculations made within the HMC sampler.
Other sts-functions:
sts_build_factored_surrogate_posterior()
,
sts_build_factored_variational_loss()
,
sts_decompose_by_component()
,
sts_decompose_forecast_by_component()
,
sts_forecast()
,
sts_one_step_predictive()
,
sts_sample_uniform_initial_state()
observed_time_series <- rep(c(3.5, 4.1, 4.5, 3.9, 2.4, 2.1, 1.2), 5) + rep(c(1.1, 1.5, 2.4, 3.1, 4.0), each = 7) %>% tensorflow::tf$convert_to_tensor(dtype = tensorflow::tf$float64) day_of_week <- observed_time_series %>% sts_seasonal(num_seasons = 7) local_linear_trend <- observed_time_series %>% sts_local_linear_trend() model <- observed_time_series %>% sts_sum(components = list(day_of_week, local_linear_trend)) states_and_results <- observed_time_series %>% sts_fit_with_hmc( model, num_results = 10, num_warmup_steps = 5, num_variational_steps = 15)
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