We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation, and are exempliied by special cases where data are modelled as a sample from mixtures of normal distributions. EEcient simulation methods are used to approximate various prior, posterior and predictive distributions. ...

This paper deals with the application of Bayesian methods to the estimation of two convective heat transfer coefficients of a roof-mounted radiant barrier system (RBS). As part of an empirical validation of the thermal model of the roofing complex, a parametric sensitivity analysis highlighted the importance of convective coefficients in the thermal behavior of a roofing complex. A parameter es...

Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to ...

We develop a model by choosing the maximum entropy distribution from the set of models satisfying certain smoothness and independence criteria; we show that inference on this model generalizes local kernel estimation to the context of Bayesian inference on stochastic processes. Our model enables Bayesian inference in contexts when standard techniques like Gaussian process inference are too expe...

Spatial data, either areal or geostatistical (point-referenced), are becoming increasingly utilized in the study of many scientific fields due to the accessibility of data monitoring systems and associated datasets. When both types of data are available for the same underlying spatial process, computationally efficient and statistically sound methods are needed for their joint analysis. Markov ...

Uniaxial compressive strength (UCS) is one of the most significant factors on the stability of underground excavation projects. Most of the time, this factor can be obtained by exploratory boreholes evaluation. Due to the large distance between exploratory boreholes in the majority of geotechnical projects, the application of geostatistical methods has increased as an estimator of rock mass pro...

This study explores the decomposition of predictive uncertainty in hydrological modeling into its contributing sources. This is pursued by developing data-based probability models describing uncertainties in rainfall and runoff data, and incorporating them into the Bayesian Total Error Analysis methodology (BATEA). A case study based on the Yzeron catchment (France) and the conceptual rainfall-...

Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains. On the other hand, estimating an MTE from data has turned out to be a difficult task, and most prevalent learning methods treat parameter estimation as a regression problem. The drawback of this approach is that by not directly attempting...

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