Question
Consider a period reversible nonstop time Markov chain having tiny change rates q furthermore, restricting probabilities {P). Allow A to mean a bunch of states
Consider a period reversible nonstop time Markov chain having tiny change rates q
furthermore, restricting probabilities {P). Allow A to mean a bunch of states for this chain, and consider another
constant time Markov chain with change rates q/given by
CLIij in the event that I A , j
something else
where c is a discretionary positive number. snow that this chain remains time reversible, and discover its
restricting probabilities.
Q58
Consider an arrangement of n segments to such an extent that the functioning occasions of segment
are dramatically dispersed with rate Ar. At the point when a segment fizzles, be that as it may, the maintenance rate ot
part I relies upon the number of different segments are down. In particular, assume that the
immediate fix pace of part j, j = 1
, n, when there are a sum of k fizzled
parts, is
(a) Explain now we can break down the former as a ceaseless time Markov chain. Characterize the
states and give the boundaries ot the chain.
(b) Show that, in consistent express, the chain is time reversible and register the restricting probabilities.
Q59
Allow Y to signify an outstanding arbitrary variable witn rate A that is autonomous ot the nonstop
time Markov chain {X(t)) and let
= jlX(O) = i}
(a) Show that
C/ikPkj +
iJ
where .is 1 when I = j and O when
(b) Show that the arrangement ot the previous set ot conditions is given by
where is the lattice ot components Pij, I is the character framework, and R the network determined in
Segment 6.9.
(c) Suppose now that VI,
{X(t)}. snow that
, Yn are autonomous exponentials with rate A that are free ot
is equivalent to the component in line I, section j ottne grid Fn.
(c) Explain tne relaonsnp 0T me going before to Approximation 2 0T area 6.9.
Q60
(a) Show that Approximation 1 of Section 6.9 is comparable to unitormizing the consistent time
Markov chain with a worth v such thatvt n and afterward approximating Pij(t) by Pij
(b) Explain why the former should make a decent estimate.
Clue: What is the standard deviation of a Poisson arbitrary variable with mean N?
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