Bayesian bootstrap was proposed by Rubin (1981) and its theoretical properties and application to survival models without covariates was studies by Lo (1993) and others. Bayesian bootstrap, empirical likelihood and bootstrap are diierent approaches based on the same idea, approximating the nonparametric model with the family of distributions whose supports are the set of observations. Based on ...

A modification of ranked set sampling (RSS) called maximum ranked set sampling with unequal sample (MRSSU) is considered for the Bayesian estimation of scale parameter α of the Weibull distribution. Under this method, we use Linex loss function, conjugate and Jeffreys prior distributions to derive the Bayesian estimate of α. In order to measure the efficiency of the obtained Bayesian estimates ...

We explore the use of proper priors for variance parameters of certain sparse Bayesian regression models. This leads to a connection between sparse Bayesian learning (SBL) models (Tipping, 2001) and the recently proposed Bayesian Lasso (Park and Casella, 2008). We outline simple modifications of existing algorithms to solve this new variant which essentially uses type-II maximum likelihood to f...

The aim of this set of exercises is to build up experience in developing Bayesian networks for realistic clinical problems. The exercises included in this assignment learn you something about the relationship between consulting Bayesian networks, using tools such as SamIam or Genie (See below), and problem solving. We start by describing two software tools for building Bayesian networks by hand...

A non-parametric hierarchical Bayesian framework is developed for designing a sophisticated classifier based on a mixture of simple (linear) classifiers. Each simple classifier is termed a local “expert”, and the number of experts and their construction are manifested via a Dirichlet process formulation. The simple form of the “experts” allows direct handling of incomplete data. The model is fu...

text classification is an important research field in information retrieval and text mining. the main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. since word detection is a difficult and time consuming task in persian language, bayesian text classifier is an appropriate approach to deal with different...

We propose a simple and effective variational inference algorithm based on stochastic optimisation that can be widely applied for Bayesian non-conjugate inference in continuous parameter spaces. This algorithm is based on stochastic approximation and allows for efficient use of gradient information from the model joint density. We demonstrate these properties using illustrative examples as well...

BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model constru...

Recent studies have proposed hierarchical Bayesian model of visual, or other multidimensional signal processing the scaling factors of linear components in which are represented by higherorder components. Here we constructed a two-layer Bayesian model of the early visual cortex and investigated the model properties to provide a explanation of how visual system acquires the even-symmetric respon...

Identifying compelled edges is important in learning the structure (i.e., the DAG) of a Bayesian network. A graphical method (Chickering 1995) was proposed to solve this problem. In this paper, we show that a joint probability distribution defined by a Bayesian network can be uniquely characterized by its intrinsic factorization. Based on such an algebraic characterization, we suggest a simple ...