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IJAR
2007
55views more  IJAR 2007»
13 years 5 months ago
Theoretical analysis and practical insights on importance sampling in Bayesian networks
The AIS-BN algorithm [2] is a successful importance sampling-based algorithm for Bayesian networks that relies on two heuristic methods to obtain an initial importance function: -...
Changhe Yuan, Marek J. Druzdzel
AAAI
2008
13 years 8 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
WSC
2007
13 years 8 months ago
Importance sampling of compounding processes
Compounding processes, also known as perpetuities, play an important role in many applications; in particular, in time series analysis and mathematical finance. Apart from some s...
Jose Blanchet, Bert Zwart
ETT
2002
142views Education» more  ETT 2002»
13 years 5 months ago
Adaptive state- dependent importance sampling simulation of markovian queueing networks
In this paper, a method is presented for the efficient estimation of rare-event (buffer overflow) probabilities in queueing networks using importance sampling. Unlike previously pr...
Pieter-Tjerk de Boer, Victor F. Nicola
IOR
2006
163views more  IOR 2006»
13 years 5 months ago
Adaptive Importance Sampling Technique for Markov Chains Using Stochastic Approximation
For a discrete-time finite-state Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This s...
T. P. I. Ahamed, Vivek S. Borkar, S. Juneja