After a brief review of previous frequentist and Bayesian approaches to multiple change-points, we describe a Bayesian model for multiple parameter changes in a multiparameter exponential family. This model has attractive statistical and computational properties and yields explicit recursive formulas for the Bayes estimates of the piecewise constant parameters. Efficient estimators of the hyper...

Step-stress model has received a considerable amount of attention in recent years. In the usual step-stress experiment, stress level is allowed to increase at each step to get rapid failure of the experimental units. The expected lifetime of the experimental unit is shortened as the stress level increases. Although, extensive amount of work has been done on step-stress models, not enough attent...

We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applicatio...

People can apparently make surprisingly sophisticated inductive inferences, despite the fact that there are constraints on cognitive resources that would make performing exact Bayesian inference computationally intractable. What algorithms could they be using to make this possible? We show that a simple sequential algorithm, Win-Stay, Lose-Shift (WSLS), can be used to approximate Bayesian infer...

In this paper we analyze a simple Bayesian heuristic for learning from others’posteriors and show its applicability to communication in groups and in social networks. The heuristic corresponds to rational Bayesian updating when individuals have conditionally independent information. When agents suffer from corelation neglect they also use the Heuristic. We show that communication in groups can ...

Simple Bayes algorithm captures the assumption that every feature is independent from the rest of the features, given the state of the class feature. The fact that the assumption of independence is clearly almost always wrong has led to a general rejection of the crude independence model in favor of more complicated alternatives, at least by researchers knowledgeable about theoretical issues. I...

There are different methods of statistical hypotheses testing. 1 – 4 Among them, a special place has Bayesian approach. A generalization of Bayesian rule of many hypotheses testing is given below. It consists in increasing of decision rule dimensionality with respect to the number of tested hypoteses, which allows to make decisions more differentiated than in the classical case and to state, in...

A popular method for creating an accurate classifier from a set of training

The problem of graduating a sequence of data values can be cast as a statistical estimation problem. In particular, the Bayesian approach is attractive due to its ability to formally incorporate known ordering and smoothness conditions for the graduated values into the estimation structure. However, this approach has not been widely adopted in practice, primarily because of the arduousness of s...

Many phenomena in animal learning can be explained by a context-learning process whereby an animal learns about different patterns of relationship between environmental variables. Differentiating between such environmental regimes or 'contexts' allows an animal to rapidly adapt its behaviour when context changes occur. The current work views animals as making sequential inferences about current...