initial_state

Given the accumulated values of the initial probabilities <code>alpha</code> and a
uniform value <code>u</code>, it returns the initial state of a Markov jump process.
This corresponds to the states satisfying cum_alpha_(k-1) &lt; u &lt; cum_alpha_(k).

Tools for phase-type distributions including the following variants:
continuous, discrete, multivariate, in-homogeneous, right-censored, and regression.
Methods for functional evaluation, simulation and estimation using the
expectation-maximization (EM) algorithm are provided for all models.
The methods of this package are based on the following references.
Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm,
Olsson, M. (1996). Estimation of phase-type distributions from censored data,
Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>,
Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>,
Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>,
Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>,
Bladt, M. (2022) <doi:10.1017/asb.2021.40>,
Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>,
Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>,
Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

Martin Bladt

matrixdist

Statistics for Matrix Distributions

Jorge Yslas

Alaric Müller

initial_state function

<dl><dt>cum_alpha</dt>
<dd>A cummulated vector of initial probabilities.</dd>
<dt>u</dt>
<dd>Random value in (0,1).</dd></dl>

Arguments

Initial state of Markov jump process — initial_state

<dl>

<dt>cum_alpha</dt>
<dd>A cummulated vector of initial probabilities.</dd>


<dt>u</dt>
<dd>Random value in (0,1).</dd>

</dl>

initial_state: Initial state of Markov jump process

Description

Usage

Value

Arguments