Poster
Fast Bidirectional Probability Estimation in Markov Models
Siddhartha Banerjee · Peter Lofgren
210 C #67
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Abstract
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Abstract:
We develop a new bidirectional algorithm for estimating Markov chain multi-step transition probabilities: given a Markov chain, we want to estimate the probability of hitting a given target state in steps after starting from a given source distribution. Given the target state , we use a (reverse) local power iteration to construct an sparse' Markov Chains -- wherein the number of transitions between states is comparable to the number of states -- the running time of our algorithm for a uniform-random target node is order-wise smaller than Monte Carlo and power iteration based algorithms; in particular, our method can estimate a probability using only running time.
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