Special Issue "Monte Carlo Methods and Algorithms"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 January 2018).

Special Issue Editor

Prof. Dr. Faming Liang
Website
Guest Editor
Department of Statistics, Purdue University, West Lafayette, IN 47906, USA
Interests: Monte carlo; big data; bioinformatics

Special Issue Information

Dear Colleagues,

The dramatic improvement in data collection and acquisition technologies in the past two decades has enabled scientists to collect vast amounts of data, such as climate data, omics data and credit card records. With growing size typically comes a growing complexity of data structures and of the models needed to account for the structures. Although Markov chain Monte Carlo has proven to be a powerful tool for analyzing the data of complex structures, its computer-intensive nature has limited its applications to big data problems. The objective of this special issue is to motivate developments of scalable Monte Carlo methods that address computational challenges in Bayesian analysis of big data. Topics of interest include (but are not limited to):

  • Distributed/parallel MCMC
  • MCMC using GPU computing
  • MCMC with split-and-merge strategies
  • Sequential Monte Carlo
  • Advantages of Bayesian Inference for Big Data
Prof. Dr. Faming Liang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Monte Carlo
  • big data
  • bioinformatics

Published Papers (2 papers)

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Research

Open AccessArticle
Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
Algorithms 2018, 11(4), 42; https://doi.org/10.3390/a11040042 - 04 Apr 2018
Cited by 2
Abstract
The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning [...] Read more.
The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order) spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE) method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them. Full article
(This article belongs to the Special Issue Monte Carlo Methods and Algorithms)
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Open AccessArticle
Generalized Kinetic Monte Carlo Framework for Organic Electronics
Algorithms 2018, 11(4), 37; https://doi.org/10.3390/a11040037 - 26 Mar 2018
Cited by 13
Abstract
In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) and light-emitting diodes (OLEDs). Our model generalizes the geometrical representation of the multifaceted properties of the organic [...] Read more.
In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) and light-emitting diodes (OLEDs). Our model generalizes the geometrical representation of the multifaceted properties of the organic material by the use of a non-cubic, generalized Voronoi tessellation and a model that connects sites to polymer chains. Herewith, we obtain a realistic model for both amorphous and crystalline domains of small molecules and polymers. Furthermore, we generalize the excitonic processes and include triplet exciton dynamics, which allows an enhanced investigation of OSCs and OLEDs. We outline the developed methods of our generalized kMC framework and give two exemplary studies of electrical and optical properties inside an organic semiconductor. Full article
(This article belongs to the Special Issue Monte Carlo Methods and Algorithms)
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