Recycling Gibbs sampling

Martino, Luca; Elvira, Vı́ctor; Camps‐Valls, Gustau

doi:10.23919/eusipco.2017.8081191

Cited by 3 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we test the sticky MCMC methods within the Recycling Gibbs (RG) sampling scheme where the intermediate samples drawn from each full-conditional pdf are sued in the final estimator [51]. We consider a simple numerical simulation (easily reproducible by any practitioner) involving a bi-dimensional target pdf …”

Section: Sticky Mcmc Methods Within Recycling Gibbs Samplingmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive independent sticky MCMC algorithms

Martino

Casarin

Leisen

et al. 2018

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

Monte Carlo methods have become essential tools to solve complex Bayesian inference problems in different fields, such as computational statistics, machine learning, and statistical signal processing. In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky Markov Chain Monte Carlo (MCMC) algorithms, to sample efficiently from any bounded target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities, which become closer and closer to the target as the number of iterations increases. The proposal pdf is built using interpolation procedures based on a set of support points which is constructed iteratively from previously drawn samples. The algorithm's efficiency is ensured by a test that supervises the evolution of the set of support points. This extra stage controls the computational cost and the convergence of the proposal density to the target. Each part of the novel family of algorithms is discussed and several examples of specific methods are provided. Although the novel algorithms are presented for univariate target densities, we show how they can be easily extended to the multivariate context by embedding them within a Gibbs-type sampler or the hit and run algorithm. The ergodicity is ensured and discussed. An overview of the related works in the literature is also provided, emphasizing that several well-known existing methods (like the adaptive rejection Metropolis sampling (ARMS) scheme) are encompassed by the new class of algorithms proposed here. Eight numerical examples (including the inference of the hyper-parameters of Gaussian processes, widely used in machine learning for signal processing applications) illustrate the efficiency of sticky schemes, both as stand-alone methods to sample from complicated one-dimensional pdfs and within Gibbs samplers in order to draw from multi-dimensional target distributions.

show abstract

Section: Sticky Mcmc Methods Within Recycling Gibbs Samplingmentioning

confidence: 99%

“…Recently, an alternative Gibbs scheme, called Recycling Gibbs (RG) sampler, has been proposed in literature [51]. The combined use of RG with a sticky algorithm is particularly interesting since RG recycles and employs all the samples drawn from each full-conditional pdfs in the final estimators.…”

Section: Recycling Gibbs Samplingmentioning

confidence: 99%

Adaptive independent sticky MCMC algorithms

Martino

Casarin

Leisen

et al. 2018

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we use Gibbs Sampling [23], [24] to estimate the DFM's parameters. First we compute the joint distribution p(w, z, t|α, β, ), and by doing this, we get the conditional probability p(z di |w, z,z ¬di , α, β, ), where z −di means the Topics exclude z di .…”

Section: B Parameter Estimationmentioning

confidence: 99%

A Network Delay Factor Model Based on the Hidden Markov Model and Latent Dirichlet Allocation

Yuchi

Yang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Network systems are widely used in today's digital communication systems and applications. Network delay is a very important factor affecting the efficiency and reliability of the system, and researchers often use a model to predict and understand the causes and patterns of delay. For our model, we propose beginning with the Hidden Markov Model (HMM) of the transmission network, which is used to analyze the network's operational situation and to predict the approximate state of the system into the future using the HMM prediction algorithm. Then, employing Latent Dirichlet Allocation (LDA), we put forward the Delay Factor Model (DFM) value for the delay. In the DFM, we need to map the delay's interval to an integer, designated as DII (Delay Interval Integer), and the factors returned define the hidden states of the HMM. From the view of the DFM, DII is generated from a factor and the previous DII randomly. We use the Gibbs Sampling approach to obtain an estimation of the DFM's parameters. By defining the HMM and DFM, we can forecast future delay with high accuracy, and the result can be shown to follow the peaks and troughs of the real operation of the network system's delay patterns.INDEX TERMS Delay factor model, derivation, hidden Markov model, prediction.

show abstract

Parsimonious adaptive rejection sampling

Martino

2017

Electron. lett.

View full text Add to dashboard Cite

Monte Carlo (MC) methods have become very popular in signal processing during the past decades. The adaptive rejection sampling (ARS) algorithms are well-known MC technique which draw efficiently independent samples from univariate target densities. The ARS schemes yield a sequence of proposal functions that converge toward the target, so that the probability of accepting a sample approaches one. However, sampling from the proposal pdf becomes more computationally demanding each time it is updated. We propose the Parsimonious Adaptive Rejection Sampling (PARS) method, where an efficient trade-off between acceptance rate and proposal complexity is obtained. Thus, the resulting algorithm is faster than the standard ARS approach.

show abstract

Recycling Gibbs sampling

Cited by 3 publications

References 35 publications

Adaptive independent sticky MCMC algorithms

Adaptive independent sticky MCMC algorithms

A Network Delay Factor Model Based on the Hidden Markov Model and Latent Dirichlet Allocation

Parsimonious adaptive rejection sampling

Contact Info

Product

Resources

About