Announcements Midterm scores (without challenge problem): –Median 85.5, mean 79, std 16. –Roughly, 85-100 ~A, 70-85 ~B, <70 ~C.

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Presentation transcript:

Announcements Midterm scores (without challenge problem): –Median 85.5, mean 79, std 16. –Roughly, ~A, ~B, <70 ~C.

Hidden Markov Models Generative, rather than descriptive model. –Objects produced by random process. Dependencies in process, some random events influence others. –Time is most natural metaphor here. Simplest, most tractable model of dependencies is Markov. Lecture based on: Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.”

Markov Chain States: S1, … SN Discrete time steps, 1, 2, … State at time t is q(t). Initial state, q(1). pi(i) = P(q(1) = Si). P(q(t) = Sj | q(t-1)= Si, q(t-2)=Sk, … ) = P(q(t) = Sj | q(t-1) = Si). This is what makes it Markov. Time independence: a(ij) = P(q(t) = Sj | q(t-1) = Si).

Examples 1D random walk in finite space. 1D curve generated by random walk in orientation.

States of Markov Chain Represent state at time t as vector: w(t) = (P(q(t)=S1), P(q(t)=S2), … P(q(t) = SN)) Put transitions, a(ij) into matrix A. –A is Stochastic, meaning columns sum to 1. Then w(t) = A*w(t-1).

Asymptotic behavior of Markov Chain w(n) = A(A(…(A(v(1))))) = A n (w(1)). –w(n) will be leading eigenvector of A. This means asymptotic behavior independent of initial conditions Some special conditions required: –Reach every state from every state (ergodic). –Markov chain may not converge (periodic)

Hidden Markov Model Observations, v(1), v(2) …, v(M). –We never know the state, but at each time step a state produces an observation. Observation distribution: b(j,k) = P(v(k) at t| q(t) = Sj). Note this is also taken to be time independent. Example, HMM that generates contours varying from smooth to rough.

Three problems Probability of observations given model. –Use to select model given observations (eg, speech recognition). –To refine estimate of HMM. Given model and observations, what were likely states? –States may have semantics (rough/smooth contour). –May provide intuitions about model. Find model to optimize probability of observations. –Learning the model.

Probability of Observations Solved with dynamic programming. Whiteboard (see Rabiner for notes).