Use Of Bayesian Approach Research Articles

Bayesian analysis for genotype x environment interactions and the GGE-biplot assessment: Evaluation of balanced classifications with missing values

In multiple environmental trials (METs) most of the data, balanced or unbalanced, are normally tested over a wide range of environments (locations, years, growing seasons, etc.) and the basic statistical method used to obtain reliable statistical information. A case study is presented here to demonstrate the usefulness of Bayesian approach in genotype-by environment data analysis, in comparison with frequentist approach and GGE biplot assessment classification with missing value. Particular emphasis was given to Bayesian application that exploits pedigree information and to the analysis of GEI data for estimation of heritability, genetic gain and means prediction. A Markov Chain Monte Carlo (MCMC) method has been considered to perform Bayesian inference using R2WinBUGS. The study recently done in sorghum variety trials show investigation can be applied for multi environmental trial data. Results shows that the Bayesian estimation of variance components was accurate compared to the frequentist. The two principal components in GGEbiplot analysis were significant, explaining 95.13% (85.17% PC1 and 9.9.% PC2) for frequentist approach and explaining 97.36% (84.06% PC1 and 13.3% PC2) for Bayesian approach of interaction variation. Bayesian analysis indicates GGE-biplot gave the best results in contributing to the GEI. Bayesian approach for analysis GEI data is highly suitable with missing values.Int J Appl Sci Biotechnol, Vol 3(2): 210-217 DOI: http://dx.doi.org/10.3126/ijasbt.v3i2.11908

Bayesian models as a unified approach to estimate relative risk (or prevalence ratio) in binary and polytomous outcomes.

BackgroundDisadvantages have already been pointed out on the use of odds ratio (OR) as a measure of association for designs such as cohort and cross sectional studies, for which relative risk (RR) or prevalence ratio (PR) are preferable. The model that directly estimates RR or PR and correctly specifies the distribution of the outcome as binomial is the log-binomial model, however, convergence problems occur very often. Robust Poisson regression also estimates these measures but it can produce probabilities greater than 1.ResultsIn this paper, the use of Bayesian approach to solve the problem of convergence of the log-binomial model is illustrated. Furthermore, the method is extended to incorporate dependent data, as in cluster clinical trials and studies with multilevel design, and also to analyse polytomous outcomes. Comparisons between methods are made by analysing four data sets.ConclusionsIn all cases analysed, it was observed that Bayesian methods are capable of estimating the measures of interest, always within the correct parametric space of probabilities.Electronic supplementary materialThe online version of this article (doi:10.1186/s12982-015-0030-y) contains supplementary material, which is available to authorized users.

Eigenvoice modelling for cross likelihood ratio based speaker clustering: A Bayesian approach

This paper proposes the use of Bayesian approaches with the cross likelihood ratio (CLR) as a criterion for speaker clustering within a speaker diarization system, using eigenvoice modelling techniques. The CLR has previously been shown to be an effective decision criterion for speaker clustering using Gaussian mixture models. Recently, eigenvoice modelling has become an increasingly popular technique, due to its ability to adequately represent a speaker based on sparse training data, as well as to provide an improved capture of differences in speaker characteristics. The integration of eigenvoice modelling into the CLR framework to capitalize on the advantage of both techniques has also been shown to be beneficial for the speaker clustering task. Building on that success, this paper proposes the use of Bayesian methods to compute the conditional probabilities in computing the CLR, thus effectively combining the eigenvoice-CLR framework with the advantages of a Bayesian approach to the diarization problem. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 33.5% relative improvement in the overall diarization error rate (DER) compared to the baseline system.

Bayesian Approaches to Copula Modelling

Copula models have become one of the most widely used tools in the applied modelling of multivariate data. Similarly, Bayesian methods are increasingly used to obtain efficient likelihood-based inference. However, to date, there has been only limited use of Bayesian approaches in the formulation and estimation of copula models. This article aims to address this shortcoming in two ways. First, to introduce copula models and aspects of copula theory that are especially relevant for a Bayesian analysis. Second, to outline Bayesian approaches to formulating and estimating copula models, and their advantages over alternative methods. Copulas covered include Archimedean, copulas constructed by inversion, and vine copulas; along with their interpretation as transformations. A number of parameterisations of a correlation matrix of a Gaussian copula are considered, along with hierarchical priors that allow for Bayesian selection and model averaging for each parameterisation. Markov chain Monte Carlo sampling schemes for fitting Gaussian and D-vine copulas, with and without selection, are given in detail. The relationship between the prior for the parameters of a D-vine, and the prior for a correlation matrix of a Gaussian copula, is discussed. Last, it is shown how to compute Bayesian inference when the data are discrete-valued using data augmentation. This approach generalises popular Bayesian methods for the estimation of models for multivariate binary and other ordinal data to more general copula models. Bayesian data augmentation has substantial advantages over other methods of estimation for this class of models.

Robust chronologies for landform development

AbstractObtaining chronological control for geomorphological sequences can be problematic due to the fragmentary and non‐sequential nature of sediment and landform archives. The robust analysis of 14C ages is often critical for the interpretation of these complicated sequences. This paper demonstrates a robust methodology for the 14C dating of geomorphological sequences using a case study from the lower Ribble valley, northwest England. The approach adopted incorporates using greater numbers of ages, targeting plant macrofossils, obtaining replicate measurements from single horizons to assess the extent of reworking and the use of Bayesian approaches to test models of the relative order of events. The extent of reworking of organic materials and space‐time dynamics of fluvial change means that it is critical that chronological control is sufficiently resourced with 14C measurements. As a result Bayesian approaches are increasingly important for the evaluation of large data sets. Assessing the conformability of relative order models informed by interpretation of the geomorphology can identify contexts or materials that are out of sequence, and focuses attention on problem materials (reworking) and errors in interpretation (outlier ages). These relative order models provide a framework for the interrogation of sequences and a means for securing probability‐based age estimates for events that occur between dated contexts. This approach has potential value in constraining the sequence of geomorphological development at scales that vary from individual sites to a catchment or region, furthering understanding of forcing and change in geomorphological systems. Copyright © 2008 John Wiley & Sons, Ltd.

Ten Years of the Relative Risk Model and Regional Scale Ecological Risk Assessment

ABSTRACT It has been 10 years since the publication of the relative risk model (RRM) for regional scale ecological risk assessment. The approach has since been used successfully for a variety of freshwater, marine, and terrestrial environments in North America, South America, and Australia. During this period the types of stressors have been expanded to include more than contaminants. Invasive species, habitat loss, stream alteration and blockage, temperature, change in land use, and climate have been incorporated into the assessments. Major developments in the RRM have included the extensive use of geographical information systems, uncertainty analysis using Monte Carlo techniques, and its application to retrospective assessments to determine causation. The future uses of the RRM include assessments for forestry and conservation management, an increasing use in invasive species evaluation, and in sustainability. Developments in risk communication, the use of Bayesian approaches, and in uncertainty analyses are on the horizon.

Bayesian Approaches for Limited Dependent Variable Change Point Problems

Limited dependent variable (LDV) data are common in political science, and political methodologists have given much good advice on dealing with them. We review some methods for LDV “change point problems” and demonstrate the use of Bayesian approaches for count, binary, and duration-type data. Our applications are drawn from American politics, Comparative politics, and International Political Economy. We discuss the tradeoffs both philosophically and computationally. We conclude with possibilities for multiple change point work.

Iowa's Experience with Road Diet Measures: Use of Bayesian Approach to Assess Impacts on Crash Frequencies and Crash Rates

A before-and-after study implemented from a Bayesian perspective to assess crash history reduction due to road diets in Iowa was conducted by the Iowa State University Department of Statistics in cooperation with the Iowa Department of Transportation's Office of Traffic and Safety. The study used both monthly crash data and estimated volumes obtained from TAS for 30 sites—15 treatment and 15 comparison-over 23 years (1982 to 2004). The sites had volumes ranging from 2,030 to 15, 350 during this period and were largely located in smaller urbanized areas. The research objective was to assess whether road diets appear to result in crash reductions on Iowa roads. Crash data were analyzed at each site before and after the conversions were completed. Given the random, rare nature of crash events, a hierarchical Poisson model was fitted, such that log mean rate was expressed as a piecewise linear function of time period, seasonal effects, and a random effect corresponding to each site. Monthly traffic volumes we...

On Bayesian Value at Risk: From Linear to Non-Linear Portfolios

This paper proposes the use of Bayesian approach to implement Value at Risk (VaR) model for both linear and non-linear portfolios. The Bayesian approach provides risk traders with the flexibility of adjusting their VaR models according to their subjective views. First, we deal with the case of linear portfolios. By imposing the conjugate-prior assumptions, a closed-form expression for the Bayesian VaR is obtained. The Bayesian VaR model can also be adjusted in order to deal with the ageing effect of the past data. By adopting Gerber-Shiu's option-pricing model, our Bayesian VaR model can also be applied to deal with non-linear portfolios of derivatives. We obtain an exact formula for the Bayesian VaR in the case of a single European call option. We adopt the method of back-testing to compare the non-adjusted and adjusted Bayesian VaR models with their corresponding classical counterparts in both linear and non-linear cases.