Probabilistic Inference

Probabilistic Inference

State Estimation and Kalman Filtering CS B659 Spring 2013 Kris Hauser Motivation Observing a stream of data Monitoring (of people, computer systems, etc) Surveillance, tracking Finance & economics Science

Questions: Modeling & forecasting Handling partial and noisy observations Markov Chains Sequence of probabilistic state variables X0,X1,X2, E.g., robots position, targets position and velocity, X0 X1 X2

X3 Observe X1 X0 independent of X2, X3, P(Xt|Xt-1) known as transition model Inference in MC Prediction: the probability of future state? P(X ) = t S =S =

S x0,,xt-1 x0,,xt-1 xt-1 P (X0,,Xt) P (X0) P

x1,,xt P(Xi|Xi-1) P(Xt|Xt-1) P(Xt-1) [Incremental approach] Blurs over time, and approaches stationary distribution as t grows Limited prediction power Rate of blurring known as mixing time

Modeling Partial Observability Hidden Markov Model (HMM) X0 X1 X2 X3 Hidden state variables

O1 O2 O3 Observed variables P(Ot|Xt) called the observation model (or sensor model) Filtering Name comes from signal processing

Goal: Compute the probability distribution over current state given observations up to this point Query variable Unknown X0 X1 X2 O1

O2 Distribution given Known Filtering Name comes from signal processing Goal: Compute the probability distribution over current state given observations up to this point P(X |o ) = t 1:t

S xt-1 P(xt-1|o1:t-1) P(Xt|xt-1,ot) P(Xt|Xt-1,ot) = P(ot|Xt-1,Xt)P(Xt|Xt-1)/P(ot|Xt-1) = a P(ot|Xt)P(Xt|Xt-1) Query variable Unknown X0 X1

X2 O1 O2 Distribution given Known Kalman Filtering In a nutshell Efficient probabilistic filtering in

continuous state spaces Linear Gaussian transition and observation models Ubiquitous for state tracking with noisy sensors, e.g. radar, GPS, cameras Hidden Markov Model for Robot Localization Use observations + transition dynamics to get a better idea of where the robot is at time t X0

X1 X2 X3 Hidden state variables z1 z2 z3

Observed variables Predict observe predict observe Hidden Markov Model for Robot Localization Use observations + transition dynamics to get a better idea of where the robot is at time t Maintain a belief state bt over time bt(x) = P(Xt=x|z1:t) X0

X1 X2 X3 Hidden state variables z1 z2 z3

Observed variables Predict observe predict observe Bayesian Filtering with Belief States Compute bt, given zt and prior belief bt Recursive filtering equation Bayesian Filtering with Belief

States Compute bt, given zt and prior belief bt Recursive filtering equation Update via the observation zt Predict P(Xt|z1:t-1) using dynamics alone In Continuous State Spaces Compute bt, given zt and prior belief bt

Continuous filtering equation In Continuous State Spaces Compute bt, given zt and prior belief bt Continuous filtering equation How to evaluate this integral? How to calculate Z? How to even represent a belief state? Key Representational Decisions Pick a method for representing distributions

Discrete: tables Continuous: fixed parameterized classes vs. particle-based techniques Devise methods to perform key calculations (marginalization, conditioning) on the representation Exact or approximate? Gaussian Distribution Mean m, standard deviation s Distribution is denoted N(m,s) If X ~ N(m,s), then With a normalization factor

Linear Gaussian Transition Model for Moving 1D Point Consider position and velocity xt, vt Time step h Without noise xt+1 = xt + h vt vt+1 = vt With Gaussian noise of std s1 P(xt+1|xt) exp(-(xt+1 (xt + h vt))2/(2s12) i.e. Xt+1 ~ N(xt + h vt, s1) Linear Gaussian Transition Model

If prior on position is Gaussian, then the posterior is also Gaussian vh s1 N(m,s) N(m+vh,s+s1) Linear Gaussian Observation Model Position observation zt Gaussian noise of std s2 zt ~ N(xt,s2)

Linear Gaussian Observation Model If prior on position is Gaussian, then the posterior is also Gaussian Posterior probability Observation probability Position prior m (s2z+s22m)/(s2+s22) s2 s2s22/(s2+s22) Multivariate Gaussians Multivariate analog in N-D space Mean (vector) m, covariance (matrix) S

With a normalization factor X ~ N(m,S) Multivariate Linear Gaussian Process A linear transformation + multivariate Gaussian noise If prior state distribution is Gaussian, then posterior state distribution is Gaussian If we observe one component of a Gaussian, then its posterior is also Gaussian y=Ax+e

e ~ N(m,S) Multivariate Computations Linear transformations of gaussians If x ~ N(m,S), y = A x + b Then y ~ N(Am+b, ASAT) Consequence If x ~ N(mx,Sx), y ~ N(my,Sy), z=x+y Then z ~ N(mx+my,Sx+Sy) Conditional of gaussian

If [x1,x2] ~ N([m1 m2],[S11,S12;S21,S22]) Then on observing x2=z, we have x1 ~ N(m1-S12S22-1(z-m2), S11-S12S22-1S21) Presentation Next time Principles Ch. 9 Rekleitis (2004)

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