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Thread: discrete markov stochastic process

  1. #1
    Jan 2009

    discrete markov stochastic process

    hi guys

    I have a simple problem driving me crazy

    the topic is stochastic process and markov process, this is the problem:

    assuming every day's weather has only two options: rainy or sunny.
    the probability that in certain day the weather is the same as the day before is p. the probability of change in weather is q=1-p.

    now i will define (x_{[n]})_{n>=0} as a discrete time stochastic process as: x_{[n]}=1 if the n'th day was rainy and x_{[n]}=0 if the n'th day was sunny.

    the question is: given that today, n=0, is a rainy day, find the probability to rain in the n'th day. clue: you can organize the data in a matrix.
    i've tried few approaches but nothing comes out, i know its simple, will be happy for assistance. thanks
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  2. #2
    Moo is offline
    A Cute Angle Moo's Avatar
    Mar 2008
    P(I'm here)=1/3, P(I'm there)=t+1/3

    Let A be the transition matrix. It's a 2x2 matrix because there are only two states. Since the probability of staying in the same state the next day is p, you will have p in the diagonal. And q otherwise.

    A=\begin{pmatrix} p&q \\ q&p\end{pmatrix}

    it can be diagonalized : its eigenvalues are 1 (eigenvector (1,1)) and p-q (eigenvector (1,-1)).
    So A=PDP^{-1}, where P=\begin{pmatrix}1&1\\1&-1\end{pmatrix} (matrix of the eigenvectors) and D=\begin{pmatrix} 1&0 \\ 0&p-q\end{pmatrix}
    Also note that P^{-1}=\frac 12\begin{pmatrix} 1&1 \\ 1&-1\end{pmatrix}

    Hence A^n=P\begin{pmatrix} 1^n & 0 \\ 0&(p-q)^n\end{pmatrix}
    But the element (i,j) in A^n denotes P(X_n=j\mid X_0=i) (more or less)
    And you're looking for P(X_n=0 \mid X_0=0), so you'll be looking at the (1,1) element of the matrix A^n
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