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Análise Bayesiana da área de olho do lombo e da espessura de gordura obtidas por ultrassom e suas associações com outras características de importância econômica na raça Nelore

Yokoo, Marcos Jun Iti
Fonte: Universidade Estadual Paulista (UNESP) Publicador: Universidade Estadual Paulista (UNESP)
Tipo: Tese de Doutorado Formato: x, 84 f. : il.
Português
Relevância na Pesquisa
46.39%
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Pós-graduação em Genética e Melhoramento Animal - FCAV; Objetivou-se com esse trabalho estimar os parâmetros genéticos para as características área de olho de lombo (AOL), espessura de gordura subcutânea na costela (EG) e espessura de gordura na garupa (EGP8) obtidas por ultrassom, ao ano (A) e ao sobreano (S). Além disso, foram estimadas as correlações genéticas entre essas características de carcaça obtidas por ultrassom (CCUS), e dessas com outras características de importância econômica em bovinos de corte, como peso (PS), altura do posterior (ALT) e perímetro escrotal (PE450) ao sobreano, idade ao primeiro parto (IPP) e primeiro intervalo entre partos (PIEP). Os parâmetros genéticos foram estimados em análises multi-características pelo modelo animal, utilizando-se a inferência Bayesiana via algoritmo de Gibbs Sampling. As estimativas de herdabilidade a posteriori para as CCUS foram: 0,46 (AOL_A), 0,42 (EG_A), 0,60 (EGP8_A), 0,33 (AOL_S), 0,59 (EG_S) e 0,55 (EGP8_S), mostrando que se essas características forem utilizadas como critério de seleção, as mesmas devem responder rapidamente à seleção individual...

Gibbs sampling and helix-cap motifs

Kruus, Erik; Thumfort, Peter; Tang, Chao; Wingreen, Ned S.
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.39%
Protein backbones have characteristic secondary structures, including α-helices and β-sheets. Which structure is adopted locally is strongly biased by the local amino acid sequence of the protein. Accurate (probabilistic) mappings from sequence to structure are valuable for both secondary-structure prediction and protein design. For the case of α-helix caps, we test whether the information content of the sequence–structure mapping can be self-consistently improved by using a relaxed definition of the structure. We derive helix-cap sequence motifs using database helix assignments for proteins of known structure. These motifs are refined using Gibbs sampling in competition with a null motif. Then Gibbs sampling is repeated, allowing for frameshifts of ±1 amino acid residue, in order to find sequence motifs of higher total information content. All helix-cap motifs were found to have good generalization capability, as judged by training on a small set of non-redundant proteins and testing on a larger set. For overall prediction purposes, frameshift motifs using all training examples yielded the best results. Frameshift motifs using a fraction of all training examples performed best in terms of true positives among top predictions. However...

Blocking Gibbs sampling for linkage analysis in large pedigrees with many loops.

Jensen, C S; Kong, A
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /09/1999 Português
Relevância na Pesquisa
46.49%
We apply the method of "blocking Gibbs" sampling to a problem of great importance and complexity-linkage analysis. Blocking Gibbs sampling combines exact local computations with Gibbs sampling, in a way that complements the strengths of both. The method is able to handle problems with very high complexity, such as linkage analysis in large pedigrees with many loops, a task that no other known method is able to handle. New developments of the method are outlined, and it is applied to a highly complex linkage problem in a human pedigree.

Influence of priors in Bayesian estimation of genetic parameters for multivariate threshold models using Gibbs sampling

Stock, Kathrin Friederike; Distl, Ottmar; Hoeschele, Ina
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
Publicado em 17/02/2007 Português
Relevância na Pesquisa
46.54%
Simulated data were used to investigate the influence of the choice of priors on estimation of genetic parameters in multivariate threshold models using Gibbs sampling. We simulated additive values, residuals and fixed effects for one continuous trait and liabilities of four binary traits, and QTL effects for one of the liabilities. Within each of four replicates six different datasets were generated which resembled different practical scenarios in horses with respect to number and distribution of animals with trait records and availability of QTL information. (Co)Variance components were estimated using a Bayesian threshold animal model via Gibbs sampling. The Gibbs sampler was implemented with both a flat and a proper prior for the genetic covariance matrix. Convergence problems were encountered in > 50% of flat prior analyses, with indications of potential or near posterior impropriety between about round 10 000 and 100 000. Terminations due to non-positive definite genetic covariance matrix occurred in flat prior analyses of the smallest datasets. Use of a proper prior resulted in improved mixing and convergence of the Gibbs chain. In order to avoid (near) impropriety of posteriors and extremely poorly mixing Gibbs chains, a proper prior should be used for the genetic covariance matrix when implementing the Gibbs sampler.

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
Publicado em 15/03/2003 Português
Relevância na Pesquisa
46.49%
A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.

Posterior analysis of stochastic frontier models using Gibbs sampling

Koop, Gary; Steel, Mark F.J.; Osiewalski, Jacek
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /12/1992 Português
Relevância na Pesquisa
66.49%
In this paper we describe the use of Gibbs sampling methods for making posterior inferences in stochastic frontier models with composed error. We show how the Gibbs sampler can greatly reduce the computational difficulties involved in analyzing such models. Our fidings are illustrated in an empirical example.

Gibbs sampling will fail in outlier problems with strong masking

Justel, Ana; Peña, Daniel
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /06/1995 Português
Relevância na Pesquisa
46.39%
This paper discusses the convergence of the Gibbs sampling algorithm when it is applied to the problem of outlier detection in regression models. Given any vector of initial conditions, theoretically, the algorithm converges to the true posterior distribution. However, the speed of convergence may slow down in a high dimensional parameter space where the parameters are highly correlated. We show that the effect of the leverage in regression models makes very difficult the convergence of the Gibbs sampling algorithm in sets of data with strong masking. The problem is illustrated in several examples.

Algoritmos adaptativos de Gibbs Sampling para la identificación de heterogeneidad en regresión y series temporales

Justel Eusebio, Ana
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Tese de Doutorado Formato: application/pdf
Português
Relevância na Pesquisa
46.49%
El objetivo principal de esta tesis doctoral es desarrollar nuevos procedimientos para la identificación de observaciones atípicas que introducen heterogeneidad en muestras con datos independientes y dependientes. Se proponen dos algoritmos diferentes para los problemas de regresión y series temporales basados en el algoritmo de Gibbs Sampling. Al igual que sucede con los métodos clásicos de identificación de valores atípicos, se demuestra que la aplicación estándar del Gibbs Sampling no proporciona una identificación correcta de estos valores atípicos en problemas que presentan grupos de observaciones atípicas enmascaradas. Dado un vector cualquiera de valores iniciales, teóricamente el algoritmo converge a la verdadera distribución a posteriori de los parámetros, sin embargo, la velocidad de convergencia puede ser extremadamente lenta cuando el espacio paramétrico tiene dimensión alta y los parámetros están muy correlacionados. Los nuevos algoritmos que se discuten en este trabajo permiten mediante un proceso de aprendizaje adaptar las condiciones iniciales del Gibbs Sampling y mejorar su convergencia a la distribución a posteriori de los parámetros del modelo.

An estimation of distribution algorithm with adaptive Gibbs sampling for unconstrained global optimization

Velasco, Jonás; Saucedo-Espinosa, Mario A.; Escalante, Hugo Jair; Mendoza, Karlo; Villarreal-Rodríguez, César Emilio; Chacón-Mondragón, Óscar L.; Rodríguez, Adrián; Berrones, Arturo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.39%
In this paper is proposed a new heuristic approach belonging to the field of evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a probability model and a set of solutions is sampled from the model which characterizes the distribution of such solutions. The main framework of the proposed method is an estimation of distribution algorithm, in which an adaptive Gibbs sampling is used to generate new promising solutions and, in combination with a local search strategy, it improves the individual solutions produced in each iteration. The Estimation of Distribution Algorithm with Adaptive Gibbs Sampling we are proposing in this paper is called AGEDA. We experimentally evaluate and compare this algorithm against two deterministic procedures and several stochastic methods in three well known test problems for unconstrained global optimization. It is empirically shown that our heuristic is robust in problems that involve three central aspects that mainly determine the difficulty of global optimization problems, namely high-dimensionality, multi-modality and non-smoothness.; Comment: This paper has been withdrawn by the author by request of the journal in which has been accepted for publication

Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

Liu, Yu; Simeone, Osvaldo; Haimovich, Alexander M.; Su, Wei
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.39%
A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.; Comment: Contains corrections with respect to the version to appear on IEEE Signal Processing Letters (see Fig. 2)

Accelerated Gibbs sampling of normal distributions using matrix splittings and polynomials

Fox, Colin; Parker, Albert
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/05/2015 Português
Relevância na Pesquisa
46.6%
Standard Gibbs sampling applied to a multivariate normal distribution with a specified precision matrix is equivalent in fundamental ways to the Gauss-Seidel iterative solution of linear equations in the precision matrix. Specifically, the iteration operators, the conditions under which convergence occurs, and geometric convergence factors (and rates) are identical. These results hold for arbitrary matrix splittings from classical iterative methods in numerical linear algebra giving easy access to mature results in that field, including existing convergence results for antithetic-variable Gibbs sampling, REGS sampling, and generalizations. Hence, efficient deterministic stationary relaxation schemes lead to efficient generalizations of Gibbs sampling. The technique of polynomial acceleration that significantly improves the convergence rate of an iterative solver derived from a \emph{symmetric} matrix splitting may be applied to accelerate the equivalent generalized Gibbs sampler. Identicality of error polynomials guarantees convergence of the inhomogeneous Markov chain, while equality of convergence factors ensures that the optimal solver leads to the optimal sampler. Numerical examples are presented, including a Chebyshev accelerated SSOR Gibbs sampler applied to a stylized demonstration of low-level Bayesian image reconstruction in a large 3-dimensional linear inverse problem.; Comment: 33 pages...

Minimum Message Length Clustering Using Gibbs Sampling

Davidson, Ian
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/01/2013 Português
Relevância na Pesquisa
46.39%
The K-Mean and EM algorithms are popular in clustering and mixture modeling, due to their simplicity and ease of implementation. However, they have several significant limitations. Both coverage to a local optimum of their respective objective functions (ignoring the uncertainty in the model space), require the apriori specification of the number of classes/clsuters, and are inconsistent. In this work we overcome these limitations by using the Minimum Message Length (MML) principle and a variation to the K-Means/EM observation assignment and parameter calculation scheme. We maintain the simplicity of these approaches while constructing a Bayesian mixture modeling tool that samples/searches the model space using a Markov Chain Monte Carlo (MCMC) sampler known as a Gibbs sampler. Gibbs sampling allows us to visit each model according to its posterior probability. Therefore, if the model space is multi-modal we will visit all models and not get stuck in local optima. We call our approach multiple chains at equilibrium (MCE) MML sampling.; Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)

Sex as Gibbs Sampling: a probability model of evolution

Watkins, Chris; Buttkewitz, Yvonne
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 11/02/2014 Português
Relevância na Pesquisa
46.39%
We show that evolutionary computation can be implemented as standard Markov-chain Monte-Carlo (MCMC) sampling. With some care, `genetic algorithms' can be constructed that are reversible Markov chains that satisfy detailed balance; it follows that the stationary distribution of populations is a Gibbs distribution in a simple factorised form. For some standard and popular nonparametric probability models, we exhibit Gibbs-sampling procedures that are plausible genetic algorithms. At mutation-selection equilibrium, a population of genomes is analogous to a sample from a Bayesian posterior, and the genomes are analogous to latent variables. We suggest this is a general, tractable, and insightful formulation of evolutionary computation in terms of standard machine learning concepts and techniques. In addition, we show that evolutionary processes in which selection acts by differences in fecundity are not reversible, and also that it is not possible to construct reversible evolutionary models in which each child is produced by only two parents.

Efficient Gibbs Sampling for Markov Switching GARCH Models

Billio, Monica; Casarin, Roberto; Osuntuyi, Anthony
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 21/12/2012 Português
Relevância na Pesquisa
46.39%
We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to existing literature is manifold. First, we discuss different multi-move sampling techniques for Markov Switching (MS) state space models with particular attention to MS-GARCH models. Our multi-move sampling strategy is based on the Forward Filtering Backward Sampling (FFBS) applied to an approximation of MS-GARCH. Another important contribution is the use of multi-point samplers, such as the Multiple-Try Metropolis (MTM) and the Multiple trial Metropolize Independent Sampler, in combination with FFBS for the MS-GARCH process. In this sense we ex- tend to the MS state space models the work of So [2006] on efficient MTM sampler for continuous state space models. Finally, we suggest to further improve the sampler efficiency by introducing the antithetic sampling of Craiu and Meng [2005] and Craiu and Lemieux [2007] within the FFBS. Our simulation experiments on MS-GARCH model show that our multi-point and multi-move strategies allow the sampler to gain efficiency when compared with single-move Gibbs sampling.; Comment: 38 pages, 7 figures

Asynchronous Distributed Gibbs Sampling

Terenin, Alexander; Simpson, Daniel; Draper, David
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 29/09/2015 Português
Relevância na Pesquisa
46.39%
Gibbs sampling is a widely used Markov Chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences. It is widely believed that MCMC methods do not extend easily to parallel implementations, as their inherently sequential nature incurs a large synchronization cost. This means that new solutions are needed to bring Bayesian analysis fully into the era of large-scale computation. In this paper, we present a novel scheme - Asynchronous Distributed Gibbs (ADG) sampling - that allows us to perform MCMC in a parallel fashion with no synchronization or locking, avoiding the typical performance bottlenecks of parallel algorithms. Our method is especially attractive in settings, such as hierarchical random-effects modeling in which each observation has its own random effect, where the problem dimension grows with the sample size. We prove convergence under some basic regularity conditions, and discuss the proof for similar parallelization schemes for other iterative algorithms. We provide three examples that illustrate some of the algorithm's properties with respect to scaling. Because our hardware resources are bounded, we have not yet found a limit to the algorithm's scaling...

On particle Gibbs sampling

Chopin, Nicolas; Singh, Sumeetpal S.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.56%
The particle Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm to sample from the full posterior distribution of a state-space model. It does so by executing Gibbs sampling steps on an extended target distribution defined on the space of the auxiliary variables generated by an interacting particle system. This paper makes the following contributions to the theoretical study of this algorithm. Firstly, we present a coupling construction between two particle Gibbs updates from different starting points and we show that the coupling probability may be made arbitrarily close to one by increasing the number of particles. We obtain as a direct corollary that the particle Gibbs kernel is uniformly ergodic. Secondly, we show how the inclusion of an additional Gibbs sampling step that reselects the ancestors of the particle Gibbs' extended target distribution, which is a popular approach in practice to improve mixing, does indeed yield a theoretically more efficient algorithm as measured by the asymptotic variance. Thirdly, we extend particle Gibbs to work with lower variance resampling schemes. A detailed numerical study is provided to demonstrate the efficiency of particle Gibbs and the proposed variants.; Comment: Published at http://dx.doi.org/10.3150/14-BEJ629 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

Quantum Gibbs Sampling Using Szegedy Operators

Tucci, Robert R.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.45%
We present an algorithm for doing Gibbs sampling on a quantum computer. The algorithm combines phase estimation for a Szegedy operator, and Grover's algorithm. For any $\epsilon>0$, the algorithm will sample a probability distribution in ${\cal O}(\frac{1}{\sqrt{\delta}})$ steps with precision ${\cal O}(\epsilon)$. Here $\delta$ is the distance between the two largest eigenvalue magnitudes of the transition matrix of the Gibbs Markov chain used in the algorithm. It takes ${\cal O}(\frac{1}{\delta})$ steps to achieve the same precision if one does Gibbs sampling on a classical computer.; Comment: V1-17 pages(8 files:1 .tex, 2 .sty, 5 .eps);V2-many minor changes to improve larity

Fast Parallel SAME Gibbs Sampling on General Discrete Bayesian Networks

Seita, Daniel; Chen, Haoyu; Canny, John
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/11/2015 Português
Relevância na Pesquisa
46.42%
A fundamental task in machine learning and related fields is to perform inference on Bayesian networks. Since exact inference takes exponential time in general, a variety of approximate methods are used. Gibbs sampling is one of the most accurate approaches and provides unbiased samples from the posterior but it has historically been too expensive for large models. In this paper, we present an optimized, parallel Gibbs sampler augmented with state replication (SAME or State Augmented Marginal Estimation) to decrease convergence time. We find that SAME can improve the quality of parameter estimates while accelerating convergence. Experiments on both synthetic and real data show that our Gibbs sampler is substantially faster than the state of the art sampler, JAGS, without sacrificing accuracy. Our ultimate objective is to introduce the Gibbs sampler to researchers in many fields to expand their range of feasible inference problems.

Quibbs, a Code Generator for Quantum Gibbs Sampling

Tucci, Robert R.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.39%
This paper introduces Quibbs v1.3, a Java application available for free. (Source code included in the distribution.) Quibbs is a "code generator" for quantum Gibbs sampling: after the user inputs some files that specify a classical Bayesian network, Quibbs outputs a quantum circuit for performing Gibbs sampling of that Bayesian network on a quantum computer. Quibbs implements an algorithm described in earlier papers, that combines various apple pie techniques such as: an adaptive fixed-point version of Grover's algorithm, Szegedy operators, quantum phase estimation and quantum multiplexors.; Comment: 21 pages (16 files: 1 .tex, 1 .sty, 14 .pdf);V2-added 2 new files(.xxx, .txt) txt file contains quibbs1.4 source code

Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width

De Sa, Christopher; Zhang, Ce; Olukotun, Kunle; Ré, Christopher
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/10/2015 Português
Relevância na Pesquisa
46.54%
Gibbs sampling on factor graphs is a widely used inference technique, which often produces good empirical results. Theoretical guarantees for its performance are weak: even for tree structured graphs, the mixing time of Gibbs may be exponential in the number of variables. To help understand the behavior of Gibbs sampling, we introduce a new (hyper)graph property, called hierarchy width. We show that under suitable conditions on the weights, bounded hierarchy width ensures polynomial mixing time. Our study of hierarchy width is in part motivated by a class of factor graph templates, hierarchical templates, which have bounded hierarchy width---regardless of the data used to instantiate them. We demonstrate a rich application from natural language processing in which Gibbs sampling provably mixes rapidly and achieves accuracy that exceeds human volunteers.