Basic POMP model components.
Mathematically, the parts of a POMP model include the latent-state process transition distribution, the measurement-process distribution, the initial-state distribution, and possibly a prior parameter distribution. Algorithmically, each of these corresponds to at least two distinct operations. In particular, for each of the above parts, one sometimes needs to make a random draw from the distribution and sometimes to evaluate the density function. Accordingly, for each such component, there are two basic model components, one prefixed by a r , the other by a d , following the usual convention.
In addition to the parts listed above, pomp
includes two additional basic model components: the deterministic skeleton, and parameter transformations that can be used to map the parameter space onto a Euclidean space for estimation purposes. There are also basic model components for computing the mean and variance of the measurement process conditional on the latent-state process.
There are thus altogether twelve basic model components :
Each of these can be set or modified in the pomp
constructor function or in any of the pomp
elementary algorithms or estimation algorithms using an argument that matches the basic model component. A basic model component can be unset by passing NULL
in the same way.
Help pages detailing each basic model component are provided.
workhorse functions , elementary algorithms , estimation algorithms .
More on implementing POMP models: Csnippet
, accumvars
, betabinomial
, covariates
, dinit_spec
, dmeasure_spec
, dprocess_spec
, emeasure_spec
, eulermultinom
, parameter_trans()
, pomp-package
, pomp_constructor
, prior_spec
, rinit_spec
, rmeasure_spec
, rprocess_spec
, skeleton_spec
, transformations
, userdata
, vmeasure_spec