"bsmc2"(object, params, Np, est, smooth = 0.1, tol = 1e-17, verbose = getOption("verbose"), max.fail = 0, transform = FALSE, ...)
"bsmc"(object, params, Np, est, smooth = 0.1, ntries = 1, tol = 1e-17, lower = -Inf, upper = Inf, verbose = getOption("verbose"), max.fail = 0, transform = FALSE, ...)
pomp
or inheriting class pomp
.
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.
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
).
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
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)
.
TRUE
, print diagnostic messages.
TRUE
, the algorithm operates on the transformed scale.
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.
pomp
, pfilter