bsmc2: The Liu and West Bayesian particle filter
In kidusasfaw/pomp: Statistical Inference for Partially Observed Markov Processes

Description Usage Arguments Details Value Filtering failures Author(s) References See Also

Modified version of the Liu and West (2001) algorithm.

## S4 method for signature 'data.frame'
bsmc2(data, Np, smooth = 0.1, tol = 1e-17,
  max.fail = 0, params, rprior, rinit, rprocess, dmeasure, partrans, ...,
  verbose = getOption("verbose", FALSE))

## S4 method for signature 'pomp'
bsmc2(data, Np, smooth = 0.1, tol = 1e-17,
  max.fail = 0, ..., verbose = getOption("verbose", FALSE))

`data`	either a data frame holding the time series data, or an object of class ‘pomp’, i.e., the output of another pomp calculation.
`Np`	number of particles
`smooth`	Kernel density smoothing parameter. The compensating shrinkage factor will be `sqrt(1-smooth^2)`. Thus, `smooth=0` means that no noise will be added to parameters. The general recommendation is that the value of `smooth` should be chosen close to 0 (e.g., `shrink` ~ 0.1).
`tol`	positive numeric scalar; particles with likelihood less than `tol` are considered to be incompatible with the data. See the section on Filtering Failures for more information.
`max.fail`	integer; the maximum number of filtering failures allowed (see below). If the number of filtering failures exceeds this number, execution will terminate with an error. By default, `max.fail` is set to infinity, so no error can be triggered.
`params`	optional; named numeric vector of parameters. This will be coerced internally to storage mode `double`.
`rprior`	optional; prior distribution sampler, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. For more information, see here. Setting `rprior=NULL` removes the prior distribution sampler.
`rinit`	simulator of the initial-state distribution. This can be furnished either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting `rinit=NULL` sets the initial-state simulator to its default. For more information, see here.
`rprocess`	simulator of the latent state process, specified using one of the rprocess plugins. Setting `rprocess=NULL` removes the latent-state simulator. For more information, see the documentation on these plugins.
`dmeasure`	evaluator of the measurement model density, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting `dmeasure=NULL` removes the measurement density evaluator. For more information, see here.
`partrans`	optional parameter transformations, constructed using `parameter_trans`. Many algorithms for parameter estimation search an unconstrained space of parameters. When working with such an algorithm and a model for which the parameters are constrained, it can be useful to transform parameters. One should supply the `partrans` argument via a call to `parameter_trans`. For more information, see here. Setting `partrans=NULL` removes the parameter transformations, i.e., sets them to the identity transformation.
`...`	additional arguments supply new or modify existing model characteristics or components. See `pomp` for a full list of recognized arguments. When named arguments not recognized by `pomp` are provided, these are made available to all basic components via the so-called userdata facility. This allows the user to pass information to the basic components outside of the usual routes of covariates (`covar`) and model parameters (`params`). See the userdata documentation here for information on how to use this facility.
`verbose`	logical; if `TRUE`, diagnostic messages will be printed to the console.

bsmc2 uses a version of the original algorithm (Liu \& West 2001), but discards the auxiliary particle filter. The modification appears to give superior performance for the same amount of effort.

Samples from the prior distribution are drawn using the rprior component. This is allowed to depend on elements of params, i.e., some of the elements of params can be treated as “hyperparameters”. Np draws are made from the prior distribution.

An object of class ‘bsmcd_pomp’. The following methods are avaiable:

plot: produces diagnostic plots
as.data.frame: puts the prior and posterior samples into a data frame

If the degree of disagreement between model and data becomes sufficiently large, a “filtering failure” results. A filtering failure occurs when, at some time point, none of the Np particles is compatible with the data. In particular, if the conditional likelihood of a particle at any time is below the tolerance value tol, then that particle is considered to be uninformative and its likelihood is taken to be zero. A filtering failure occurs when this is the case for all particles. A warning is generated when this occurs unless the cumulative number of failures exceeds max.fail, in which case an error is generated.

Michael Lavine, Matthew Ferrari, Aaron A. King, Edward L. Ionides

Liu, J. and M. West. Combining Parameter and State Estimation in Simulation-Based Filtering. In A. Doucet, N. de Freitas, and N. J. Gordon, editors, Sequential Monte Carlo Methods in Practice, pages 197-224. Springer, New York, 2001.

Other particle filter methods: cond.logLik, eff.sample.size, filter.mean, filter.traj, mif2, pfilter, pmcmc, pred.mean, pred.var