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In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms. While Wang-Landau and othe...
Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs
Machine Learning (stat.ML) Neural and Evolutionary Computing (cs.NE)
2010/12/17
Restricted Boltzmann Machines (RBM) have attracted a lot of attention of late, as one the principle building blocks of deep networks. Training RBMs remains problematic however, because of the intracti...
Importance Tempering
simulated tempering importance sampling Markov chain Monte Carlo(MCMC) Metropolis–coupled MCMC
2010/4/30
Simulated tempering (ST) is an established Markov chain Monte Carlo (MCMC)
method for sampling from a multimodal density (). Typically, ST involves introducing
an auxiliary variable k taking value...