newState

Previous state of the Markov jump process

previousState

cumulatedEmbeddedMC

Given a transition matrix <code>Q</code>, a uniform value <code>u</code>, and a previous state <code>k</code>, it returns the new state of a Markov jump process

Tools for homogeneous and in-homogeneous phase-type distributions.
Methods for functional evaluation, simulation and estimation using the
expectation-maximization (EM) algorithm are provided.
The methods of this package are based on the following references.
Asmussen, S., Nerman, O., & Olsson, M. (1996) <https://www.jstor.org/stable/4616418>,
Olsson, M. (1996) <https://www.jstor.org/stable/4616419>.
Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>
Albrecher, H., Bladt, M., & Yslas, J. (2020) <doi:10.1111/sjos.12505>
Bladt, M., & Yslas, J. (2020) <arXiv:2011.03219>.

Martin Bladt

matrixdist

Statistics for Matrix Distributions

Jorge Yslas

newState function

<dl><dt>previousState</dt>
<dd>Previous state of the Markov jump process</dd>
<dt>cumulatedEmbeddedMC</dt>
<dd>A transition matrix</dd>
<dt>u</dt>
<dd>A random value in (0,1)</dd></dl>

Arguments

New state in a Markov jump process — newState

<dl>

<dt>previousState</dt>
<dd>Previous state of the Markov jump process</dd>


<dt>cumulatedEmbeddedMC</dt>
<dd>A transition matrix</dd>


<dt>u</dt>
<dd>A random value in (0,1)</dd>

</dl>

newState: New state in a Markov jump process

Description

Usage

Value

Arguments