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21:10
YouTube
Eike Decker
Everyone can understand transformation matrices - and how it works
Transformation matrices are essential to computer graphics - reading and understanding them is often thought to be complicated when in fact it is really simple once you take the right look into how these matrices work.
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Let P^(1) and P^(2) denote transition probability matrices for ergodic Markov chains having the same state space. Let π^1 and π^2 denote the stationary (limiting) probability vectors for the two chains. Consider a process defined as follows: (i) X0=1. A coin is then flipped and if it comes up heads, then the remaining states X1, …are obtained from the transition probability matrix P^(1) and if tails from the matrix P^(2). Is {Xn, n ⩾0} a Markov chain? If p=P{ coin comes up heads }, what is limn
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