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IPPS
2008
IEEE
14 years 3 months ago
Reducing the run-time of MCMC programs by multithreading on SMP architectures
The increasing availability of multi-core and multiprocessor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Mont...
Jonathan M. R. Byrd, Stephen A. Jarvis, A. H. Bhal...
IJCNN
2000
IEEE
14 years 1 months ago
On MCMC Sampling in Bayesian MLP Neural Networks
Bayesian MLP neural networks are a flexible tool in complex nonlinear problems. The approach is complicated by need to evaluate integrals over high-dimensional probability distri...
Aki Vehtari, Simo Särkkä, Jouko Lampinen
VLSISP
2002
123views more  VLSISP 2002»
13 years 8 months ago
Monte Carlo Bayesian Signal Processing for Wireless Communications
Abstract. Many statistical signal processing problems found in wireless communications involves making inference about the transmitted information data based on the received signal...
Xiaodong Wang, Rong Chen, Jun S. Liu
JDA
2007
77views more  JDA 2007»
13 years 8 months ago
Path coupling without contraction
Path coupling is a useful technique for simplifying the analysis of a coupling of a Markov chain. Rather than defining and analysing the coupling on every pair in Ω×Ω, where...
Magnus Bordewich, Martin E. Dyer
ML
2007
ACM
192views Machine Learning» more  ML 2007»
13 years 8 months ago
Annealing stochastic approximation Monte Carlo algorithm for neural network training
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
Faming Liang