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The relationship between gross domestic product and monetary variables in Romania. A Bayesian approach

ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA 2017年第1期30卷 464-476页

作者： Simionescu, Mihaela Popescu, Jenica Firescu, Victoria Romanian Acad Inst Econ Forecasting Bucharest Romania Univ Craiova Dept Finance Banking & Econ Anal Fac Econ & Business Adm Craiova Romania Univ Pitesti Dept Finance Accounting & Econ Fac Econ Jurid & Adm Sci Pitesti Romania

For establishing the suitable monetary policy it is essential to know if there is a relevant relationship in practice between gross domestic product (G.D.P.) variations and monetary variables. The purpose of this study is to analyse the causality between output variation and money aggregate in Romania for quarterly data in the period 2000:Q1-2015:Q2. Moreover the impact on G.D.P. growth of other variables connected with money demand is assessed using Bayesian techniques. The results indicated a bidirectional relationship between G.D.P. variations and rate of real money demand in the mentioned period. The Granger causality test combined with stochastic search variable selection indicated that active interest rate and discount rata mostly explained G.D.P. variations. According to results based on Bayesian regime-switching models, contrary to expectations, the interest rate increases continued to generate higher output variations, the consumption being the engine of economic growth in Romania. In periods of economic recession, the lower interest rate stimulated the recovery of the economy.

关键词： GDP gibbs sampling algorithm posterior mean Bayesian model regime-switching model

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Bayesian nonparametric forecasting for INAR models

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2016年 100卷 70-78页

作者： Bisaglia, Luisa Canale, Antonio Univ Turin Dept Econ & Stat Corso Unione Sovietica 218bis I-10134 Turin Italy Collegio Carlo Alberto Moncalieri Italy Univ Padua Dept Stat Sci I-35100 Padua Italy

A nonparametric Bayesian method for producing coherent predictions of count time series with the nonnegative integer-valued autoregressive process is introduced. Predictions are based on estimates of h-step-ahead predictive mass functions, assuming a nonparametric distribution for the innovation process. That is, the distribution of errors are modeled by means of a Dirichlet process mixture of rounded Gaussians. This class of prior has large support on the space and probability mass functions and can generate almost any kind of count distribution, including over/under-dispersion and multimodality. An efficient gibbs sampler is developed for posterior computation, and the method is used to analyze a dataset of visits to a web site. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Count time series INAR(1) Dirichlet process mixtures Forecasting gibbs sampling algorithm

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The Analysis of QQ Group Topic based on the LDA 5

The Analysis of QQ Group Topic based on the LDA

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5th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Qiu, Jin Di, Xiaoqiang Yin, Dali Bi, Lin Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781509021291

In the group of instant messaging software like Tencent QQ, there are too many messages and it is hard to quickly grasp the useful messages concerned with the members. In order to solve this problem, we collect more than 300,000 messages from four active QQ groups for three months,and analyze the hot topics by LDA (Latent Dirichlet Allocation) model. At the same time, the topic probabilities are estimated by gibbs sampling algorithm, so it is convenient for members to quickly understand the useful messages in the QQ group.

关键词： LDA Topic Perplexity Function gibbs sampling algorithm

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Application with a hybrid ant colony optimisation in motif detecting problem

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第2期44卷 88-93页

作者： Zhang, Yi Zhang, Meng Pei, Zhili Jilin Business & Technol Coll Dept Comp Sci Changchun 130062 Peoples R China Air Force Aviat Univ Mil Simulat Technol Inst Changchun 130022 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist E Changchun 130012 Peoples R China Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028000 Peoples R China Jilin Univ Coll Math Changchun 130012 Peoples R China

In this paper, a hybrid optimisation algorithm for the motif detection problem of biological sequences is presented. Our method is improved gibbs sampling method by employing an improved ant colony optimisation (ACO) algorithm. The goal of our method is to reduce the required computing time and get better solution. First, we find a set of better candidate positions for revising the motif by using an improved ACO. Then we use these candidate positions as the input to the gibbs sampling method. The simulation results show that by employing our improved algorithm, both efficiency and quality for detecting motifs are improved compared with simple gibbs sampling method.

关键词： ant colony optimisation gibbs sampling algorithm motif detection problem

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Detection of copy number variation from array intensity and sequencing read depth using a stepwise Bayesian model

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BMC BIOINFORMATICS 2010年第1期11卷 1-13页

作者： Zhang, Zhengdong D. Gerstein, Mark B. Albert Einstein Coll Med Dept Genet Bronx NY 10461 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Yale Univ Interdept Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA

Background: Copy number variants (CNVs) have been demonstrated to occur at a high frequency and are now widely believed to make a significant contribution to the phenotypic variation in human populations. Array-based comparative genomic hybridization (array-CGH) and newly developed read-depth approach through ultrahigh throughput genomic sequencing both provide rapid, robust, and comprehensive methods to identify CNVs on a whole-genome scale. Results: We developed a Bayesian statistical analysis algorithm for the detection of CNVs from both types of genomic data. The algorithm can analyze such data obtained from PCR-based bacterial artificial chromosome arrays, high-density oligonucleotide arrays, and more recently developed high-throughput DNA sequencing. Treating parameters-e. g., the number of CNVs, the position of each CNV, and the data noise level-that define the underlying data generating process as random variables, our approach derives the posterior distribution of the genomic CNV structure given the observed data. sampling from the posterior distribution using a Markov chain Monte Carlo method, we get not only best estimates for these unknown parameters but also Bayesian credible intervals for the estimates. We illustrate the characteristics of our algorithm by applying it to both synthetic and experimental data sets in comparison to other segmentation algorithms. Conclusions: In particular, the synthetic data comparison shows that our method is more sensitive than other approaches at low false positive rates. Furthermore, given its Bayesian origin, our method can also be seen as a technique to refine CNVs identified by fast point-estimate methods and also as a framework to integrate array-CGH and sequencing data with other CNV-related biological knowledge, all through informative priors.

关键词： Posterior Distribution Markov Chain Monte Carlo Markov Chain Monte Carlo Simulation Reversible Jump Markov Chain Monte Carlo gibbs sampling algorithm

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The value of position-specific priors in motif discovery using MEME

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BMC BIOINFORMATICS 2010年第1期11卷 1-14页

作者： Bailey, Timothy L. Boden, Mikael Whitington, Tom Machanick, Philip Univ Queensland Inst Mol Biosci Brisbane Qld 4072 Australia

Background: Position-specific priors have been shown to be a flexible and elegant way to extend the power of gibbs sampler-based motif discovery algorithms. Information of many types-including sequence conservation, nucleosome positioning, and negative examples-can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM). Results: We extend the popular EM-based MEME algorithm to utilize position-specific priors and demonstrate their effectiveness for discovering transcription factor (TF) motifs in yeast and mouse DNA sequences. Utilizing a discriminative, conservation-based prior dramatically improves MEME's ability to discover motifs in 156 yeast TF ChIP-chip datasets, more than doubling the number of datasets where it finds the correct motif. On these datasets, MEME using the prior has a higher success rate than eight other conservation-based motif discovery approaches. We also show that the same type of prior improves the accuracy of motifs discovered by MEME in mouse TF ChIP-seq data, and that the motifs tend to be of slightly higher quality those found by a gibbs sampling algorithm using the same prior. Conclusions: We conclude that using position-specific priors can substantially increase the power of EM-based motif discovery algorithms such as MEME algorithm.

关键词： Expectation Maximization Motif Discovery Negative Sequence Candidate Motif gibbs sampling algorithm

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Equivalence of multibreed animal models and hierarchical Bayes analysis for maternally influenced traits

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GENETICS SELECTION EVOLUTION 2010年第1期42卷 20-20页

作者： Munilla Leguizamon, Sebastian Cantet, Rodolfo J. C. Univ Buenos Aires Fac Agron Dept Anim Prod Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

Background: It has been argued that multibreed animal models should include a heterogeneous covariance structure. However, the estimation of the (co) variance components is not an easy task, because these parameters can not be factored out from the inverse of the additive genetic covariance matrix. An alternative model, based on the decomposition of the genetic covariance matrix by source of variability, provides a much simpler formulation. In this study, we formalize the equivalence between this alternative model and the one derived from the quantitative genetic theory. Further, we extend the model to include maternal effects and, in order to estimate the (co) variance components, we describe a hierarchical Bayes implementation. Finally, we implement the model to weaning weight data from an Angus x Hereford crossbred experiment. Methods: Our argument is based on redefining the vectors of breeding values by breed origin such that they do not include individuals with null contributions. Next, we define matrices that retrieve the null-row and the null-column pattern and, by means of appropriate algebraic operations, we demonstrate the equivalence. The extension to include maternal effects and the estimation of the (co) variance components through the hierarchical Bayes analysis are then straightforward. A FORTRAN 90 gibbs sampler was specifically programmed and executed to estimate the (co) variance components of the Angus x Hereford population. Results: In general, genetic (co) variance components showed marginal posterior densities with a high degree of symmetry, except for the segregation components. Angus and Hereford breeds contributed with 50.26% and 41.73% of the total direct additive variance, and with 23.59% and 59.65% of the total maternal additive variance. In turn, the contribution of the segregation variance was not significant in either case, which suggests that the allelic frequencies in the two parental breeds were similar. Conclusion: The multibreed m

关键词： Variance Component Breed Group gibbs sampling algorithm Breed Origin Marginal Posterior Density

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Bayesian estimation and unit root tests for random coefficient autoregressive models

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Model Assisted Statistics and Applications 2008年第4期3卷 281-295页

作者： Wang, Dazhe Ghosh, Sujit K. Sanofi-Aventis Malvern PA 19355 9 Great Valley Parkway United States Department of Statistics North Carolina State University Raleigh NC United States

Random Coefficient AutoRegressive (RCAR) models are obtained by introducing random coefficients to an AR or more generally ARMA model. These models have second order properties similar to that of ARCH and GARCH models. In this article, we consider a Bayesian approach to estimate RCAR models. We study the frequentist performance of the Bayes estimators and show that they have good coverage properties. We propose a couple of Bayesian testing criteria for the unit root hypothesis of first order RCAR models: one is based on the Posterior Interval, and the other one is based on the Bayes Factor. Using extensive simulation studies, we evaluate the performance of the Bayesian tests and show the impact of using different priors. In the end, we present a real life example involving the daily stock volume transaction data to illustrate the proposed methods. © 2008 IOS Press. All rights reserved.

关键词： Bayes factor gibbs sampling algorithm Posterior interval Stationarity parameter Unit root testing

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Design of a combinatorial DNA microarray for protein-DNA interaction studies

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BMC BIOINFORMATICS 2006年第1期7卷 429-429页

作者： Mintseris, Julian Eisen, Michael B. Boston Univ Bioinformat Program Boston MA 02215 USA Univ Calif Berkeley Lawrence Berkeley Lab Dept Genome Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Mol & Cell Biol Berkeley CA 94720 USA

Background: Discovery of precise specificity of transcription factors is an important step on the way to understanding the complex mechanisms of gene regulation in eukaryotes. Recently, double-stranded protein-binding microarrays were developed as a potentially scalable approach to tackle transcription factor binding site identification. Results: Here we present an algorithmic approach to experimental design of a microarray that allows for testing full specificity of a transcription factor binding to all possible DNA binding sites of a given length, with optimally efficient use of the array. This design is universal, works for any factor that binds a sequence motif and is not species-specific. Furthermore, simulation results show that data produced with the designed arrays is easier to analyze and would result in more precise identification of binding sites. Conclusion: In this study, we present a design of a double stranded DNA microarray for protein-DNA interaction studies and show that our algorithm allows optimally efficient use of the arrays for this purpose. We believe such a design will prove useful for transcription factor binding site identification and other biological problems.

关键词： Array Probe Intergenic Sequence Combinatorial Design Probe Length gibbs sampling algorithm

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Markov modelling of changes in HIV-specific cytotoxic T-lymphocyte responses with time in untreated HIV-1 infected patients

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STATISTICS IN MEDICINE 2003年第10期22卷 1675-1690页

作者： Kousignian, I Autran, B Chouquet, C Calvez, V Gomard, E Katlama, C Rivière, Y Costagliola, D Hop La Pitie Salpetriere INSERM EMI 0214 F-75625 Paris 13 France Hop La Pitie Salpetriere INSERM U543 F-75625 Paris France Hop La Pitie Salpetriere Virol Lab F-75625 Paris 13 France Hop La Pitie Salpetriere Serv Malad Infect & Trop F-75625 Paris 13 France CHU Cochin Port Royal INSERM U445 Paris France Inst Pasteur Dept Sida & Retrovirus Paris France CNRS UMR C5583 Lab Stat & Probabil Toulouse France

HIV-specific cytotoxic CD8(+) T-lymphocytes (CTL) appear to be the cornerstone of the immune response to HIV infection. Recent studies show that CTL activity reflects patients' anti-HIV immune status and slows disease progression. However, the dynamics of the diversity of this response also appears as a key parameter for immune control but the dynamics of this diversity is largely undocumented. We modelled changes in CTL responses against the seven principal HIV proteins over time. We also studied the influence of plasma viral load on temporal changes in HIV protein recognition by memory CTL. The generic model we developed is based on a continuous time homogeneous Markov process with reversible states. Those states are defined by the number of proteins recognized by memory CTL in a given patient at a given time. This approach was developed within a Bayesian framework. Full Bayesian inference is implemented using Markov chain Monte Carlo simulations (MCMC). The gibbs sampling algorithm was used to estimate the marginal posterior distributions of the transition intensities between stages of CTL responses. We applied our model to data of 152 HIV-infected patients included in the IMMUNOCO cohort. The model suggested that the diversity of HIV protein recognition by memory CTL in treatment-naive patients decreases as the disease progresses. Namely, the loss of T cytotoxic responses is globally faster than their acquisition. Indeed, these patients' T cytotoxic responses were characterized by marked individual turnover and a gradual loss of multiple protein recognition over time, this loss accelerating as viral load increased. Copyright (C) 2003 John Wiley Sons, Ltd.

关键词： cytotoxic T-lymphocytes (CTL) Markov process Bayesian inference Monte Carlo Markov chains (MCMC) gibbs sampling algorithm

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