expectedRewardsBeforeHittingA

Given a markovchain object and reward values for every state,
function calculates expected reward value for a set A of states after n 
steps.

Functions and S4 methods to create and manage discrete time Markov
chains more easily. In addition functions to perform statistical (fitting
and drawing random variates) and probabilistic (analysis of their structural
proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>.
Some functions for continuous times Markov chains depends on the suggested ctmcd package, that,
as May 2023, can be retrieved from <https://cran.r-project.org/src/contrib/Archive/ctmcd/ctmcd_1.4.2.tar.gz>.

Giorgio Spedicato

markovchain

Easy Handling Discrete Time Markov Chains

Giorgio Alfredo Spedicato

Tae Seung Kang

Sai Bhargav Yalamanchi

Mildenberger Thoralf

Deepak Yadav

Ignacio Cordón

Vandit Jain

Toni Giorgino

Richèl J.C. Bilderbeek

Daniel Ebbert

Shreyash Maheshwari

expectedRewardsBeforeHittingA function

<dl><dt>markovchain</dt>
<dd>the markovchain-class object</dd>
<dt>A</dt>
<dd>set of states for first passage expected reward</dd>
<dt>state</dt>
<dd>initial state</dd>
<dt>rewards</dt>
<dd>vector depicting rewards coressponding to states</dd>
<dt>n</dt>
<dd>no of steps of the process</dd></dl>

Arguments

Sai Bhargav Yalamanchi, Vandit Jain

Author

Expected first passage Rewards for a set of states in a markovchain — expectedRewardsBeforeHittingA

<dl>

<dt>markovchain</dt>
<dd>the markovchain-class object</dd>


<dt>A</dt>
<dd>set of states for first passage expected reward</dd>


<dt>state</dt>
<dd>initial state</dd>


<dt>rewards</dt>
<dd>vector depicting rewards coressponding to states</dd>


<dt>n</dt>
<dd>no of steps of the process</dd>

</dl>

Expected first passage Rewards for a set of states in a markovchain

expectedRewardsBeforeHittingA: Expected first passage Rewards for a set of states in a markovchain

Description

Usage

Value

Arguments

Author

Details