2004•Journal of Nanjing University of Science and TechnologyRequires access

Inverse Problem of a Class of Discrete Time Phase-Type Distribution

Nie Pan-hong

Open publisher page 0 citations

Abstract

The paper studies the inverse problem of discrete time phase-type distribution from the conditional distribution vector sequence.Under knowing the conditional distribution vector sequence of Markov chain’s first arrival time,this paper determines the transient state transfer matrix by means of matrix analysis.

About this research paper

What this paper is about

The paper studies the inverse problem of discrete time phase-type distribution from the conditional distribution vector sequence.Under knowing the conditional distribution vector sequence of Markov chain’s first arrival time,this paper determines the transient state transfer matrix by means of matrix analysis.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The paper studies the inverse problem of discrete time phase-type distribution from the conditional distribution vector sequence.Under knowing the conditional distribution vector sequence of Markov chain’s first arrival time,this paper determines the transient state transfer matrix by means of matrix analysis.

Key concepts: Discrete phase-type distribution, Phase-type distribution, Markov chain, Mathematics, Sequence (biology), Distribution (mathematics), Inverse, Conditional probability distribution

Related papers

Back to paper searchBrowse research topicsOriginal source
Inverse Problem of a Class of Discrete Time Phase-Type Distribution — Research Paper | ScholarLens