December 2013
Intermediate to advanced
208 pages
5h 40m
English
book
Fast Sequential Monte Carlo Methods for Counting and Optimization
by Reuven Y. Rubinstein, Ad Ridder, Radislav Vaisman
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Index
n-step-look-ahead
adaptive splitting
associated stochastic problem
asymptotic optimality
basic Monte Carlo
biadjacency matrix
binary contingency tables
Boltzmann distribution
bounded relative error
capture-recapture
complexity theory
confidence interval
crude Monte Carlo
decreasing sets
degeneracy
direct estimation
elite set
entropy
cross-
differential
joint
Kullback-Leibler
relative
Shannon
exponential change of measure
exponential-time estimator
fixed level splitting
fully polynomial-time randomized approximation scheme
Gibbs sampler
graph coloring
Hamilton cycles
importance sampling
independent sets
Jensen's inequality
knapsack problem
Kullback-Leibler divergence
likelihood ratio
logarithmic efficiency
Markov decision process
mastermind game
max-cut problem
maximum likelihood
maximum satisfiability problem
minimum cross-entropy
basic
classic
general
indicator
Jaynes
parametric
single
multi-level approach
multiple-event probability
multiple-OSLA
nominal parameter
one-step-look-ahead
optimization
combinatorial
continuous
noisy
oracle
parameterized family
parametric cross-entropy\hb minimization
perfect matching
permanent
permutation Monte Carlo
Pincus theorem
polynomial-time estimator
prior pdf
rare-event probability
rarity parameter
reference parameter
reinforcment learning
relative error
reliability model
RSA problem
sample performance function
satisfiability problem
screening
self-avoiding walk
set covering problem
smoothed updating
splitting method
splitting parameter ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.