Lecture 20 Notes

# 6 08 1 12 gibbs example 1 05 0 05 1 15 1 05 0 05 1 15

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Unformatted text preview: ), … Proposal Q: ‣ pick a block i uniformly (or round robin, or any other schedule) ‣ sample XB(i) ~ P(XB(i) | X¬B(i)) Gibbs example 0.8 0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 0.8 0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 1 1.2 Gibbs example 1 0.5 0 0.5 1 1.5 1 0.5 0 0.5 1 1.5 Why is Gibbs useful? P (x￿ , x￿ i ) P (xi | x￿ i ) i ¬ ¬ For Gibbs, p = P (xi , x¬i ) P (x￿ | x¬i ) i Gibbs derivation ￿ ￿ P (xi , x¬i ) = = = ￿ x¬i ) P (xi | P (xi , x¬i ) P (x￿ | x¬i ) i P (x￿ , x¬i ) P (xi | x¬i ) i P (xi , x¬i ) P (x￿ | x¬i ) i P (x￿ , x¬i ) P (xi , x¬i )/P (x¬i ) i P (xi , x¬i ) P (x￿ , x¬i )/P (x¬i ) i 1 Gibbs in practice Proof of p=1 means Gibbs is often easy to implement Often works well ‣ if we choose good blocks (but there may be no good blocking!) Fancier version: adaptive blocks, based on current x Gibbs failure example 5 4 3 2 1 0 1 2 3 4 5 6 4 2 0 2 4 6 Sequential sampling In an HMM or DBN, to sample P(XT), start from X1 and sample forward step by step ‣ Xt+1 ~ P(Xt+1 | Xt) P(X1:T) = P(X1) P(X2 | X1) P(X3 | X2) … Particle ﬁlter Can sample Xt+1 ~ P(Xt+1 | Xt) using any algorithm from above If we use parallel importance sampling to get N samples at once from each P(Xt), we get a particle ﬁlter ‣ also need one more trick: resampling Write xt,i (i = 1…N) for sample at time t Particle ﬁlter Want one sample from each of P(Xt+1 | xt,i) Have only Z P(Xt+1 | xt,i) For each i, pick xt+1,i from proposal Q(x) Compute unnormalized importance weight wi = ZP (xt+1,i | xt,i )/Q(xt+1,i ) ˆ Particle ﬁlter Normalize weights: 1￿ w= ¯ wi ˆ Ni wi = wi /w ˆ¯ Now, (wi, xt+1,i) is an approximate weighted sample from P(Xt+1) What will happen if we do this for T=1, 2, … ? Resampling To get an unweighted sample, resample Sample N times (with replacement) from xt+1,i with probabilities wi/N ‣ alternately: deterministically take ﬂoor(wi) copies of xt+1,i and sample only from fractional part [wi – ﬂoor(wi)] Each xt+1,i appears wi times on average, so we’re still a sample from P(Xt+1) Particle ﬁlter example Learning Learning Basic learning problem: given some experience, ﬁnd a new or improved model Experience: a sample x1, …, xN Model: want to predict xN+1, … Example Experience = range sensor readings & odometry from ro...
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## This note was uploaded on 01/24/2014 for the course CS 15-780 taught by Professor Bryant during the Fall '09 term at Carnegie Mellon.

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