Population Monte Carlo algorithms.

Iba, Yukito

doi:10.1527/tjsai.16.279

Cited by 84 publications

(86 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As previously mentioned, a possible solution to this sampling problem is available through the use of population MCMC methods, see e.g. (Iba, 2000;Liang and Wong, 2001;Laskey and Myers, 2003;Jasra et al, 2007). Such population MCMC methods can be very efficient in the context of model comparison because not only do they allow sampling from highly nonlinear multimodal posterior distributions, but the usually redundant samples taken from intermediate temperatures may also be reused in the estimation of the marginal likelihood using thermodynamic integration (Friel and Pettitt, 2008).…”

Section: The Goodwin Model Of Biochemical Oscillatory Controlmentioning

confidence: 99%

Estimating Bayes factors via thermodynamic integration and population MCMC

Calderhead

Girolami

2009

Computational Statistics & Data Analysis

150

178

View full text Add to dashboard Cite

Section: The Goodwin Model Of Biochemical Oscillatory Controlmentioning

confidence: 99%

Estimating Bayes factors via thermodynamic integration and population MCMC

Calderhead

Girolami

2009

Computational Statistics & Data Analysis

150

178

View full text Add to dashboard Cite

“…The population annealing method was first discussed by Iba [5] in the general context of population-based algorithms and later applied to spin glasses by Hukushima and Iba [6]. More recently, Machta [7] used a method that avoids the recording of weight functions through population control in every step.…”

Section: Algorithmmentioning

confidence: 99%

“…One recent approach of this type is the population annealing (PA) algorithm [5,6]. There, a large number of configurations are prepared in independent equilibrium configurations, for instance at infinite temperature.…”

Section: Introductionmentioning

confidence: 99%

GPU accelerated population annealing algorithm

Barash

Weigel

Borovský

et al. 2017

Computer Physics Communications

View full text Add to dashboard Cite

Population annealing is a promising recent approach for Monte Carlo simulations in statistical physics, in particular for the simulation of systems with complex free-energy landscapes. It is a hybrid method, combining importance sampling through Markov chains with elements of sequential Monte Carlo in the form of population control. While it appears to provide algorithmic capabilities for the simulation of such systems that are roughly comparable to those of more established approaches such as parallel tempering, it is intrinsically much more suitable for massively parallel computing. Here, we tap into this structural advantage and present a highly optimized implementation of the population annealing algorithm on GPUs that promises speed-ups of several orders of magnitude as compared to a serial implementation on CPUs. While the sample code is for simulations of the 2D ferromagnetic Ising model, it should be easily adapted for simulations of other spin models, including disordered systems. Our code includes implementations of some advanced algorithmic features that have only recently been suggested, namely the automatic adaptation of temperature steps and a multi-histogram analysis of the data at different temperatures. The program calculates the internal energy, specific heat, several magnetization moments, entropy and free energy of the 2D Ising model on square lattices of edge length L with periodic boundary conditions as a function of inverse temperature β. Solution method: The code uses population annealing, a hybrid method combining Markov chain updates with population control. The code is implemented for NVIDIA GPUs using the CUDA language and employs advanced techniques such as multi-spin coding, adaptive temperature steps and multi-histogram reweighting. Restrictions: The system size and size of the population of replicas are limited depending on the memory of the GPU device used. Unusual features: Additional comments: Code repository at https://github.com/LevBarash/PAising. Running time: For the default parameter values used in the sample programs, L = 64, θ = 100, β0 = 0, β f = 1, ∆β = 0.005, R = 20 000, a typical run time on an NVIDIA Tesla K80 GPU is 151 seconds for the single spin coded (SSC) and 17 seconds for the multi-spin coded (MSC) program (see Sec. 2 for a description of these parameters). PROGRAM SUMMARY

show abstract

“…This leads to the Population Monte Carlo (PMC) algorithm, following Iba's (2000) denomination. The essence of the PMC scheme is to learn from experience, that is, to build an importance sampling function based on the performances of earlier importance sampling proposals.…”

Section: Population Monte Carlo Approximationsmentioning

confidence: 99%

Bayesian Modelling and Inference on Mixtures of Distributions

Marin¹,

Mengersen²,

Robert³

2005

Handbook of Statistics

330

364

View full text Add to dashboard Cite

'But, as you have already pointed out, we do not need any more disjointed clues,' said Bartholomew. 'That has been our problem all along: we have a mass of small facts and small scraps of information, but we are unable to make any sense out of them. The last thing we need is more.' Susanna Gregory, A Summer of Discontent IntroductionToday's data analysts and modellers are in the luxurious position of being able to more closely describe, estimate, predict and infer about complex systems of interest, thanks to ever more powerful computational methods but also wider ranges of modelling distributions. Mixture models constitute a fascinating illustration of these aspects: while within a parametric family, they offer malleable approximations in non-parametric settings; although based on standard distributions, they pose highly complex computational challenges; and they are both easy to constrain to meet identifiability requirements and fall within the class of ill-posed problems. They also provide an endless benchmark for assessing new techniques, from the EM algorithm to reversible jump methodology. In particular, they exemplify the

show abstract

Population Monte Carlo algorithms.

Cited by 84 publications

References 42 publications

Estimating Bayes factors via thermodynamic integration and population MCMC

Estimating Bayes factors via thermodynamic integration and population MCMC

GPU accelerated population annealing algorithm

Bayesian Modelling and Inference on Mixtures of Distributions

Contact Info

Product

Resources

About