probhmm: Conditional Distribution Function of DTHMM
In HiddenMarkov: Hidden Markov Models

Description Usage Arguments Details Value See Also

Calculates the distribution function at each point for a dthmm process given the complete observed process except the given point.

1	probhmm(logalpha, logbeta, Pi, delta, cumprob)

`logalpha`	an nm* matrix containing the logarithm of the forward probabilities.
`logbeta`	an nm* matrix containing the logarithm of the backward probabilities.
`Pi`	is the mm* transition probability matrix of the hidden Markov chain.
`delta`	is the marginal probability distribution of the m hidden states at the first time point.
`cumprob`	an nm* matrix where the (i,k)th element is Pr{Xi <= xi \| Ck = ck}.

Let X^{(-i)} denote the entire process, except with the point Xi removed. The distribution function at the point Xi is

Pr{ Xi <= xi | X^{(-i)} = x^{(-i)} } .

This R function calculates the distribution function for each point Xi for i=1, ..., n. This is done by using the forward and backward probabilities before and after the ith point, respectively.

In the programming code, note the subtraction of the mean. This is to stop underflow when the exponential is taken. Removal of the mean is automatically compensated for by the fact that the same factor is removed in both the numerator and denominator.

A vector containing the probability.

residuals

HiddenMarkov documentation built on April 27, 2021, 5:06 p.m.