CS221: HMM and Particle Filters

CS 221: Artificial Intelligence Lecture 5: Hidden Markov Models and Temporal Filtering Sebastian Thrun and Peter Norvig Slide credit: Dan Klein, Michael Pfeiffer

Class-On-A-Slide X 5 X 2 E 1 X 1 X 3 X 4 E 2 E 3 E 4 E 5

Overview Markov Chains Hidden Markov Models Particle Filters More on HMMs

Reasoning over Time Often, we want to reason about a sequence of observations Speech recognition Robot localization User attention Medical monitoring Financial modeling

Markov Models A Markov model is a chain-structured BN Each node is identically distributed (stationarity) Value of X at a given time is called the state As a BN: Parameters: called transition probabilities or dynamics, specify how the state evolves over time (also, initial probs) X 2 X 1 X 3 X 4

Conditional Independence Basic conditional independence: Past and future independent of the present Each time step only depends on the previous This is called the Markov property Note that the chain is just a (growing) BN We can always use generic BN reasoning on it if we truncate the chain at a fixed length X 2 X 1 X 3 X 4

Example: Markov Chain Weather: States: X = {rain, sun} Transitions: Initial distribution: 1.0 sun What ’s the probability distribution after one step? rain sun 0.9 0.9 0.1 0.1 This is a CPT, not a BN!

Mini-Forward Algorithm Question: What ’s P(X) on some day t? An instance of variable elimination! sun rain sun rain sun rain sun rain Forward simulation

Example From initial observation of sun From initial observation of rain P( X 1 ) P( X 2 ) P( X 3 ) P( X  ) P( X 1 ) P( X 2 ) P( X 3 ) P( X  )

Stationary Distributions If we simulate the chain long enough: What happens? Uncertainty accumulates Eventually, we have no idea what the state is! Stationary distributions: For most chains, the distribution we end up in is independent of the initial distribution Called the stationary distribution of the chain Usually, can only predict a short time out

Example: Web Link Analysis PageRank over a web graph Each web page is a state Initial distribution: uniform over pages Transitions: With prob. c, uniform jump to a random page (dotted lines, not all shown) With prob. 1-c, follow a random outlink (solid lines) Stationary distribution Will spend more time on highly reachable pages Google 1.0 returned the set of pages containing all your keywords in decreasing rank, now all search engines use link analysis along with many other factors (rank actually getting less important over time)

Hidden Markov Models Markov chains not so useful for most agents Eventually you don’t know anything anymore Need observations to update your beliefs Hidden Markov models (HMMs) Underlying Markov chain over states S You observe outputs (effects) at each time step As a Bayes net: X 5 X 2 E 1 X 1 X 3 X 4 E 2 E 3 E 4 E 5

Example: Robot Localization t=0 Sensor model: never more than 1 mistake Motion model: may not execute action with small prob. 1 0 Prob Example from Michael Pfeiffer

Example: Robot Localization t=1 1 0 Prob

Hidden Markov Model HMMs have two important independence properties: Markov hidden process, future depends on past via the present Current observation independent of all else given current state Quiz: does this mean that observations are mutually independent? [No, correlated by the hidden state] X 5 X 2 E 1 X 1 X 3 X 4 E 2 E 3 E 4 E 5

Inference in HMMs (Filtering) E 1 X 1 X 2 X 1

Example An HMM is defined by: Initial distribution: Transitions: Emissions:

Particle Filtering Sometimes |X| is too big to use exact inference |X| may be too big to even store B(X) E.g. X is continuous |X| 2 may be too big to do updates Solution: approximate inference Track samples of X, not all values Samples are called particles Time per step is linear in the number of samples But: number needed may be large In memory: list of particles, not states This is how robot localization works in practice 0.0 0.1 0.0 0.0 0.0 0.2 0.0 0.2 0.5

Representation: Particles Our representation of P(X) is now a list of N particles (samples) Generally, N << |X| Storing map from X to counts would defeat the point P(x) approximated by number of particles with value x So, many x will have P(x) = 0! More particles, more accuracy For now, all particles have a weight of 1 Particles: (3,3) (2,3) (3,3) (3,2) (3,3) (3,2) (2,1) (3,3) (3,3) (2,1)

Particle Filtering: Elapse Time Each particle is moved by sampling its next position from the transition model This is like prior sampling – samples ’ frequencies reflect the transition probs Here, most samples move clockwise, but some move in another direction or stay in place This captures the passage of time If we have enough samples, close to the exact values before and after (consistent)

Particle Filtering: Observe Slightly trickier: Don ’t do rejection sampling (why not?) We don ’t sample the observation, we fix it This is similar to likelihood weighting, so we downweight our samples based on the evidence Note that, as before, the probabilities don ’t sum to one, since most have been downweighted (in fact they sum to an approximation of P(e))

Particle Filtering: Resample Rather than tracking weighted samples, we resample N times, we choose from our weighted sample distribution (i.e. draw with replacement) This is analogous to renormalizing the distribution Now the update is complete for this time step, continue with the next one Old Particles: (3,3) w=0.1 (2,1) w=0.9 (2,1) w=0.9 (3,1) w=0.4 (3,2) w=0.3 (2,2) w=0.4 (1,1) w=0.4 (3,1) w=0.4 (2,1) w=0.9 (3,2) w=0.3 New Particles: (2,1) w=1 (2,1) w=1 (2,1) w=1 (3,2) w=1 (2,2) w=1 (2,1) w=1 (1,1) w=1 (3,1) w=1 (2,1) w=1 (1,1) w=1

Sensor Information: Importance Sampling

Particle Filter Algorithm Sample the next generation for particles using the proposal distribution Compute the importance weights : weight = target distribution / proposal distribution Resampling: “Replace unlikely samples by more likely ones”

Particle Filter Algorithm Algorithm particle_filter ( S t-1 , u t-1 z t ): For Generate new samples Sample index j(i) from the discrete distribution given by w t-1 Sample from using and Compute importance weight Update normalization factor Insert For Normalize weights

Other uses of HMM Find most likely sequence of states Viterbi algorithm Learn HMM parameters from data Baum-Welch (EM) algorithm Other types of HMMs Continuous, Gaussian-linear: Kalman filter Structured transition/emission probabilities: Dynamic Bayes network (DBN)

Real HMM Examples Speech recognition HMMs: Observations are acoustic signals (continuous valued) States are specific positions in specific words (so, tens of thousands) Machine translation HMMs: Observations are words (tens of thousands) States are translation options (dozens per word) Robot tracking: Observations are range readings (continuous) States are positions on a map (continuous)

HMM Application Domain: Speech Speech input is an acoustic wave form s p ee ch l a b Graphs from Simon Arnfield ’s web tutorial on speech, Sheffield: http://www.psyc.leeds.ac.uk/research/cogn/speech/tutorial/ “ l” to “a” transition:

Learning Problem Given example observation trajectories umbrella, umbrella, no-umbrella, umbrella no-umbrella, no-umbrella, no-umbrella … Given: structure of HMM Problem: Learn probabilities P(x), P(x’|x), P(z|x)

Let’s first learn a Markov Chain 3 Episodes (R=rain, S=sun) S, R, R, S, S, S, S, S, S, S R, S, S, S, S, R, R, R, R, R S, S, S, R, R, S, S, S, S, S Initial probability P(S) = 2/3 State transition probability P(S’|S) = 5/6 P(R’|R) = 2/3

CS221: HMM and Particle Filters

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CS221: HMM and Particle Filters