Suppose that whether or not it rains today depends on previous
weather conditions through the last two days.
Specifically, suppose that if it has rained for the past two
days, then it will rain tomorrow with probability 0.7; if it
rained
today but not yesterday, then it will rain tomorrow with
probability 0.5; if it rained yesterday but not today, then it
will
rain tomorrow with probability 0.4; if it has not rained in
the past two days, then it will
rain tomorrow with probability 0.2. If we let the state at
time n depend only on whether or not it is raining at time n,
Q1. ---whether the preceding model is a Markov chain or not?
And why? .
And we can transform this model into a Markov chain by saying
that the state at any time is determined by the weather
conditions during both that day and the previous day. In other
words, we can say that the process is in
state0 if it rained both today and yesterday,
state1 if it rained today but not yesterday,
state2 if it rained yesterday but not today,
state3 if it did not rain either yesterday or today.
Q2.---The preceding would then represent a four-state Markov
chain having a transition probability matrix
P, and find P, plz?
Q3. ---Given that it rained on Monday and Tuesday, what is the
probability that it will rain on Thursday?