getNewParams.SC: Solve for new parameters in restricted model in EM algorithm.

In DOBAD: Analysis of Discretely Observed Linear Birth-and-Death(-and-Immigration) Markov Chains

Description

Basically one step of the EM algorithm. Given old parameters and the data, get the new parameters.

Usage

getNewParams.SC(theData,oldParams, beta.immig,  dr = 0.001, r=4,n.fft =
1024, prec.tol, prec.fail.stop)

Arguments

oldParams	Parameters from previous iteration
beta.immig	immigrationrate = beta.immig * birthrate
theData	The discretely observed BDI process. Of class CTMC_PO_many, CTMC_PO_1, list.
dr	tuning parameter for differentiation
r	Parameter for differentiation; see numDeriv package documentation.
n.fft
prec.tol	"Precision tolerance"; to compute conditional means, first the joint means are computed and then they are normalized by transition probabilities. The precision parameters govern the conditions under which the function will quit if these values are very small. If the joint-mean is smaller than prec.tol then the value of prec.fail.stop decides whether to stop or continue.
prec.fail.stop	If true, then when joint-mean values are smaller than prec.tol the program stops; if false then it continues, usually printing a warning.

DOBAD documentation built on May 2, 2019, 3:04 a.m.