Sequential monte carlo samplers

About

This paper shows how one can use Sequential Monte Carlo methods to perform what is typically done using Markov chain Monte Carlo methods. This leads to a general class of principled integration and genetic type optimization methods based on interacting particle systems.

Pierre Del Moral, Arnaud Doucet · 2002

Related benchmarks

Task	Dataset	Result
Unconditional modeling	Funnel d = 10	Delta log Z0.561	30
Unconditional modeling	25GMM d = 2	Delta Log Z0.569	30
Unconditional modeling	Manywell d = 32	Δ log Z14.99	29
Target Distribution Sampling	Funnel 10D	Sinkhorn Distance149.35	29
Toy target distribution sampling	GMM40 d = 50	W2 (Entropy Regulated, eps=0.05)111.81	18
Sampling on discretised synthetic densities	Manywell d = 32	Sinkhorn Dist.29.14	15
Sampling from synthetic distributions	25GMM d = 2	Delta Log Partition Function Error (Zr)0.345	13
Sampling from synthetic distributions	Manywell d = 32	Partition Function Error (Zr)30.17	13
Learning Continuous Target Distributions	MoS d = 50	Sinkhorn Cost3297.28	11
Target Distribution Sampling	Many-Well 5D	Sinkhorn Distance20.71	11