# Probabilities in markov chain

I have problem with calculating the probability of Markov Chain with 3 states S = {0,1,2}.

I need to calculate $P(X_1=1,X_2=1|X_0=2)$.

In the answers to my workbook I am given solution:

$P(X_1=1,X_2=1|X_0=2) = P(X_2=1|X_1=1,X_0=2)P(X_1=1|X_0=2)P(X_2=1|X_1=1)P(X_1=1|X_0=2),$

but I have trouble understanding what happened in this step (I guess total probability is used, but I don't really get how).

There is also a transition matrix give and starting distribution, but I'm not sure if they are needed here.

Thanks a lot for help.

There should be another equal sign; the solution should look like this:

\begin{align}P(X_1=1,X_2=1|X_0=2) &= P(X_2=1|X_1=1,X_0=2)P(X_1=1|X_0=2)\\ &=P(X_2=1|X_1=1)P(X_1=1|X_0=2) \end{align}

where the first equality uses Bayes rule/the definition of conditional probability, applied partially; compare this with $P(X_2,X_1)=P(X_2|X_1)P(X_1)$

(or multiply both sides of $P(X_1=1,X_2=1|X_0=2) = P(X_2=1|X_1=1,X_0=2)P(X_1=1|X_0=2)$ by $P(X_0=2)$ by zero to check that the equality is true.)

while the second equality uses the Markov property.

• How should I understand this first statement: $P(X_1=1,X_2=1|X_0=2) ?$ Does it mean Prob of (A and B) under condition C or is it Prob of A and (B under condition C)? – Przemysław Robert Wilk May 26 '15 at 21:40
• That refers to the probability of A and B, conditioned on C. In this context, you are at state 2 at time 0, and are interested in the next two states being 1 at times 1 and 2 respectively. (On the other hand, you could think of \$P(X_2=1 | X_1=1, X_0=2) as: you are currently at state 1 at time 1, and you have the historical information that you were in state 2 at time 0; you want the probability of moving to state 1 at time 2.) – Ken Wei May 26 '15 at 21:54
• Thank's a lot Ken, this explanation really made me understand! – Przemysław Robert Wilk May 26 '15 at 22:07