A Bayesian method for estimating uncertainty in excavated material

Bayesian melding for estimating uncertainty in national HIV prevalence estimates

OBJECTIVE To construct confidence intervals for HIV prevalence in countries with generalised epidemics. METHODS In the Bayesian melding approach, a sample of country-specific epidemic curves describing HIV prevalence over time is derived based on time series of antenatal clinic prevalence data and general information on the parameters that describe the HIV epidemic. The prevalence trends at a...

A nonparametric Bayesian method for estimating a response function

Consider the problem of estimating a response function which depends upon a non-stochastic independent variable under our control. The data are independent Bernoulli random variables where the probabilities of success are given by the response function at the chosen values of the independent variable. Here we present a nonparametric Bayesian method for estimating the response function. The only...

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks

A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems

Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty. Bayesian averaging also allows experts to estimate the uncertainty accurately when a ...

Estimating Classification Uncertainty of Bayesian Decision TreeTechnique on Financial Data

Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in which risks have to be estimated. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the diversity of a classifier ensemble...