Kick-off
Announcements Materials are up to date on website Go over names New Material Re do MATLAB Examples Example 4 Data from slides "Human" "Computer" "Interaction" Go over Assignment 03 Intro to hidden Markov Models Markov Model |
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Hidden Markov Model Doubly embedded stochastic process Coin toss example |
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Free parameters Model representational ability Computational Complexity Urn and Ball example |
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HMM is characterized by N, number of states in the model S, {S_1,S_2,S_3} state at time t is q_t M the number of distinct observation symbols per state V = {v_1,V_2,V_3} State transition probability A={a_ij} a_ij = P(q_t+1 = S_j | q_t = S_i) 1<= i, j <= N if all states can reach all states then a_ij > 0 for all i,m The Observation symbol probability distribution in state j B = {b_j(k)} b_j(k) = P(v_k at t | q_t = S_j) 1<= j <=N , 1<=k <=M The initial state distribution pi = {pi_i} pi_i = P(q_1=S_i) 1<= i <=N Example of how to use it generatively Model can be completely determined by M, N, A, B, and pi