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

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

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

## S4 method for signature 'pomp'
bsmc2(object, params, Np, est, smooth = 0.1,
     tol = 1e-17, seed = NULL,
     verbose = getOption("verbose"), max.fail = 0,
     transform = FALSE, ...)
## S4 method for signature 'pomp'
bsmc(object, params, Np, est, smooth = 0.1,
     ntries = 1, tol = 1e-17, lower = -Inf, upper = Inf, seed = NULL,
     verbose = getOption("verbose"), max.fail = 0,
     transform = FALSE, ...)

`object`	An object of class `pomp` or inheriting class `pomp`.
`params, Np`	Specifications for the prior distribution of particles. See details below.
`est`	Names of the rows of `params` that are to be estimated. No updates will be made to the other parameters. If `est` is not specified, all parameters for which there is variation in `params` will be estimated.
`smooth`	Kernel density smoothing parameters. The compensating shrinkage factor will be `sqrt(1-smooth^2)`. Thus, `smooth=0` means that no noise will be added to parameters. Generally, the value of `smooth` should be chosen close to 0 (i.e., `shrink~0.1`).
`ntries`	Number of draws from `rprocess` per particle used to estimate the expected value of the state process at time `t+1` given the state and parameters at time `t`.
`tol`	Particles with log likelihood below `tol` are considered to be “lost”. A filtering failure occurs when, at some time point, all particles are lost. When all particles are lost, the conditional log likelihood at that time point is set to be `log(tol)`.
`lower, upper`	optional; lower and upper bounds on the priors. This is useful in case there are box constraints satisfied by the priors. The posterior is guaranteed to lie within these bounds.
`seed`	optional; an object specifying if and how the random number generator should be initialized (‘seeded’). If `seed` is an integer, it is passed to `set.seed` prior to any simulation and is returned as the “seed” element of the return list. By default, the state of the random number generator is not changed and the value of `.Random.seed` on the call is stored in the “seed” element of the return list.
`verbose`	logical; if `TRUE`, print diagnostic messages.
`max.fail`	The maximum number of filtering failures allowed. If the number of filtering failures exceeds this number, execution will terminate with an error.
`transform`	logical; if `TRUE`, the algorithm operates on the transformed scale.
`...`	currently ignored.

There are two ways to specify the prior distribution of particles. If params is unspecified or is a named vector, Np draws are made from the prior distribution, as specified by rprior. Alternatively, params can be specified as an npars x Np matrix (with rownames).

bsmc uses version of the original algorithm that includes a plug-and-play auxiliary particle filter. bsmc2 discards this auxiliary particle filter and appears to give superior performance for the same amount of effort.

An object of class “bsmcd.pomp”. The “params” slot of this object will hold the parameter posterior medians. The slots of this class include:

`post`	A matrix containing draws from the approximate posterior distribution.
`prior`	A matrix containing draws from the prior distribution (identical to `params` on call).
`eff.sample.size`	A vector containing the effective number of particles at each time point.
`smooth`	The smoothing parameter used (see above).
`seed`	The state of the random number generator at the time `bsmc` was called. If the argument `seed` was specified, this is a copy; if not, this is the internal state of the random number generator at the time of call.
`nfail`	The number of filtering failures encountered.
`cond.log.evidence`	A vector containing the conditional log evidence scores at each time point.
`log.evidence`	The estimated log evidence.
`weights`	The resampling weights for each particle.

Michael Lavine (lavine at math dot umass dot edu), Matthew Ferrari (mferrari at psu dot edu), Aaron A. King (kingaa at umich dot edu), Edward L. Ionides (ionides at umich dot edu)

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.

pomp, pfilter

pomp documentation built on May 2, 2019, 4:09 p.m.