Prepare to sample with manual scheduling

Run an MCMC, but schedule execution of the chains yourself. Use this if you want to distribute chains over (say) the nodes of an HPC system. The arguments are the same as for monty_sample, except that the runner argument is missing as you will be looking after that yourself. After using this function, you will generally be wanting to run monty_sample_manual_run and monty_sample_manual_collect.

Usage

monty_sample_manual_prepare(
  model,
  sampler,
  n_steps,
  path,
  initial = NULL,
  n_chains = 1L,
  burnin = NULL,
  thinning_factor = NULL
)

Arguments

model: The model to sample from; this should be a monty_model for now, but we might change this in future to test to see if things match an interface rather than a particular class attribute.
sampler: A sampler to use. These will be described later, but we hope to make these reasonably easy to implement so that we can try out different sampling ideas. For now, the only sampler implemented is monty_sampler_random_walk().
n_steps: The number of steps to run the sampler for.
path: The path to write inputs and outputs to. This should be a path to a directory which does not yet exist, or which is empty; we will create one here. The contents of this directory are managed by monty and the names and contents of files here are an implementation detail and should not be relied on. Calling monty_sample_manual_cleanup() will delete the directory in its entirety. Be aware that if you use tempfile() here (which can be a reasonable choice!) that this path will be deleted when your R process ends, so if using these the process calling monty_sample_manual_prepare should outlive running all sampling.
initial: Optionally, initial parameter values for the sampling. If not given, we sample from the model (or its prior). Alternatively, you can provide a monty_samples object here – the result of a previous call to this function – and we will sample some starting points from the final portion of the chains (the exact details here are subject to change, but we'll sample from the last 20 points or 5% of the chain, which ever smaller, with replacement, pooled across all chains in the previous sample).
n_chains: Number of chains to run. The default is to run a single chain, but you will likely want to run more.
burnin: Number of steps to discard as burnin. This affects only the recording of steps as your chains run; we don't record the first burnin steps. Generally you would want to do this in post-processing with monty_samples_thin() as this data is discarded with no chance of getting it back. However, if your observation process creates a large amount of data, then you may prefer to apply a burnin here to reduce how much memory is used.
thinning_factor: A thinning factor to apply while the chain is running. If given, then we save every thinning_factor'th step. So if thinning_factor = 2 we save every second step, and if 10, we'd save every 10th. Like burnin above, it is preferable to apply this in post processing with monty_samples_thin(). However, for slow-mixing chains that have a large observer output you can use this to reduce the memory usage. Use of thinning_factor requires that n_steps is an even multiple of thinning_factor; so if thinning_factor is 10, then n_steps must be a multiple of 10. This ensures that the last step is in the sample. The thinning factor cannot be changed when continuing a chain.

Value

Invisibly, the path used to store files (the same as the value of the path argument)

Details

In contrast to monty_sample there is no runner argument here, because by using this function directly you are taking responsibility for being your own runner.

As with the ways of running a set of chains in monty, it is expected that using monty_sample_manual_* will result in the same samples being generated as if you had used monty_sample with a runner of your choice.

Examples


model <- monty_example("banana")
sampler <- monty_sampler_random_walk(vcv = diag(2) * 0.05)
path <- tempfile()
monty_sample_manual_prepare(model, sampler, 100, path)
monty_sample_manual_info(path)
#> 
#> ── Manual monty sampling at /tmp/RtmpNZNAL2/file16437049d0a3 ───────────────────
#> ℹ Created 2024-12-03 14:46:01
#> ℹ 100 steps x 1 chains
#> ✖ No chains complete

# Run the (single) chain
monty_sample_manual_run(1, path)
monty_sample_manual_info(path)
#> 
#> ── Manual monty sampling at /tmp/RtmpNZNAL2/file16437049d0a3 ───────────────────
#> ℹ Created 2024-12-03 14:46:01
#> ℹ 100 steps x 1 chains
#> ✔ All chains complete

# Collect the results
monty_sample_manual_collect(path)
#> 
#> ── <monty_samples: 2 parameters x 100 samples x 1 chain> ───────────────────────
#> ℹ Parameters: 'alpha' and 'beta'
#> ℹ Conversion to other types is possible:
#> → ✔ posterior::as_draws_array() [package loaded]
#> → ✔ posterior::as_draws_df() [package loaded]
#> → ✔ coda::as.mcmc.list() [package loaded]
#> ℹ See `?monty_sample()` and `vignette("samples")` for more information

# Clean up samples
monty_sample_manual_cleanup(path)

Usage

Arguments

Value

Details

See also

Examples