In this paper, we discuss different predictors of times to failure of units censored in a hybrid censored sample from exponential distribution. Bayesian and non-Bayesian point predictors for the times to failure of units are obtained. Non-Bayesian prediction Intervals are obtained based on pivotal and highest conditional density methods. Bayesian prediction intervals are also proposed. One real...

This article gives a basic introduction to the principles of Bayesian inference in a machine learning context, with an emphasis on the importance of marginalisation for dealing with uncertainty. We begin by illustrating concepts via a simple regression task before relating ideas to practical, contemporary, techniques with a description of ‘sparse Bayesian’ models and the ‘relevance vector machi...

Highlights • Orders-of-magnitude improvement in approximate Bayesian inference efficiency • Bitstream autocorrelation limits inference approximation accuracy • Autocorrelation successfully mitigated to improve Bayesian inference approximation • Approximate Bayesian inference efficiently performed in hardware 2 Abstract Advancements in autonomous robotic systems have been impeded by the lack of ...

Is the mind, by design, predisposed against performing Bayesian inference? Previous research on base rate neglect suggests that the mind lacks the appropriate cognitive algorithms. However, any claim against the existence of an algorithm, Bayesian or otherwise, is impossible to evaluate unless one specifies the information format in which it is designed to operate. The authors show that Bayesia...

We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, h, is a neural network which learns to transform a simple noise distribution, p( ) = N (0, I), to a distribution q(θ) . = q(h( )) over the parameters θ of another neural network (the "primary network"). We train q with variational inference, using an invertible h to ena...

BAYESIAN DATA ANALYSIS is superseding traditional methods in sciences from anthropology to zoology. Bayesian methods solve many problems inherent in p values and confidence intervals. More importantly, Bayesian methods are more richly and intuitively informative. Bayesian analysis applies flexibly and seamlessly to simple situations or complex hierarchical models and realistic data structures, ...

In this paper, we present a Non-Bayesian conditioning rule for belief revision. This rule is truly Non-Bayesian in the sense that it doesn’t satisfy the common adopted principle that when a prior belief is Bayesian, after conditioning by X, Bel(X|X) must be equal to one. Our new conditioning rule for belief revision is based on the proportional conflict redistribution rule of combination develo...

The naive Bayesian classiier provides a simple and eeective approach to classiier learning, but its attribute independence assumption is often violated in the real world. A number of approaches have sought to alleviate this problem. A Bayesian tree learning algorithm builds a decision tree, and generates a local naive Bayesian classiier at each leaf. The tests leading to a leaf can alleviate at...

We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image di!erences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from D...