Dry matter grain yield per plot from three genetically homogeneous single-cross maize hybrids were analysed to investigate whether environmental variance depends on genotype. Three genotypes were tested at 20 locations in 3 years. The data were analysed using a non-parametric approach and fully parametric Bayesian models. Both analyses reveal effects of genotype on environmental variation. The ...

We suggest using a pair of metrics which quantify the extent to which the prior and likelihood functions influence inferences of parameters within a parametric Bayesian model, one of which is closely related to the reference prior of Berger and Bernardo. Our hope is that the utilization of these metrics will allow for the precise quantification of prior and likelihood information and mitigate t...

In this paper, we merge the parametric structure of neural networks into a segmental semi-Markov model to set up a Bayesian framework for protein structure prediction. The parametric model, which can also be regarded as an extension of a sigmoid belief network, captures the underlying dependency in residue sequences. The results of numerical experiments indicate the usefulness of this approach.

In this paper we provide Bayesian matching methods for finding the causal effect of a binary intake variable x ∈ {0, 1} on an outcome of interest y. One technique we introduce is a Bayesian variant of the classic Rosenbaum and Rubin (1983, 1984) propensity score matching method. We show how it is possible to find the posterior distribution of the Bayesian matched sample average treatment effect...

Functional magnetic resonance imaging (fMRI) has led to enormous progress in human brain mapping. Adequate analysis of the massive spatiotemporal data sets generated by this imaging technique, combining parametric and non-parametric components, imposes challenging problems in statistical modelling. Complex hierarchical Bayesian models in combination with computer-intensive Markov chain Monte Ca...

In this paper, we offer a gentle introduction to Gaussian processes for time-series data analysis. The conceptual framework of Bayesian modelling for time-series data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes. We discuss how domain knowledge influences design of the Gaussian process models and provide case examples to highlight the ap...

In this paper, we present two classes of Bayesian approaches to the twosample problem. Our first class of methods extends the Bayesian t-test to include all parametric models in the exponential family and their conjugate priors. Our second class of methods uses Dirichlet process mixtures (DPM) of such conjugate-exponential distributions as flexible nonparametric priors over the unknown distribu...

2.1. Bayesian Inference Is Reallocation of Credibility Across Possibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.1.1 Data are noisy and inferences are probabilistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2. Possibilities Are Parameter Values in Descriptive Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....

The essential role of the likelihood function in both Bayesian and non-Bayesian inference is described. Several topics related to the extension of likelihood-based methodology to more complex settings are reviewed, including modifications to profile likelihood, composite and pseudo-likelihoods, quasi-likelihood, semiparametric and non-parametric likelihoods, and empirical likelihood .  2010 Jo...

A new, nonparametric, approach to Bayesian robustness is presented. Whereas many studies in Bayesian robustness have dealt with a parametric sampling distribution, considering classes of prior distributions on the parameters, here we assume that the sampling distribution comes from a Dirichlet process with a parameter η = βα, with β > 0 and α being a probability measure, specified with uncertai...