In this work, we propose a novel approach towards sequential data modeling that leverages the strengths of hidden Markov models and echo-state networks (ESNs) in the context of nonparametric Bayesian inference approaches. We introduce a non-stationary hidden Markov model, the time-dependent state transition probabilities of which are driven by a high-dimensional signal that encodes the whole hi...

Joint Monitoring of Several Types of Shocks in Dynamic Linear Models: A Bayesian Decision Approach Abstract In this paper we consider the sequential monitoring process in Dynamic Linear Models as a Bayesian sequential decision problem. By using this approach we build a general procedure that jointly analyses the existence of outliers, level changes, variance changes and development of local cor...

In this paper, we propose a new generative approach for semantic slot filling task in spoken language understanding using a nonparametric Bayesian formalism. Slot filling is typically formulated as a sequential labeling problem, which does not directly deal with the posterior distribution of possible slot values. We present a nonparametric Bayesian model involving the generation of arbitrary na...

We present the explicit solution of the Bayesian problem of sequential testing of two simple hypotheses about the intensity of an observed Poisson process. The method of proof consists of reducing the initial problem to a free-boundary differential-difference Stephan problem, and solving the latter by use of the principles of smooth and continuous fit. A rigorous proof of the optimality of the ...

Common software release procedures based on statistical techniques try to optimise the trade-off between further testing costs and costs due to remaining errors. We propose new software release procedures where the aim is to certify that the software does not contain errors. The underlying model is a new discrete-time model similar to the JelinskiMoranda model. The decisions are based on a mix ...

In this paper, we propose a methodology to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time using Sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make para...

We discuss a Root Cause Analysis (RCA) system implementing a probabilistic approach based on Bayesian inference for adaptive reasoning under uncertainties in industrial process operation. The proposed approach is model based and accumulates the process knowledge within the problem domain, which data is gathered and stored in XML-based information server. The Bayesian networks have been created ...

There has been a surge of research interest in developing tools and analysis for Bayesian optimization, the task of finding the global maximizer of an unknown, expensive function through sequential evaluation using Bayesian decision theory. However, many interesting problems involve optimizing multiple, expensive to evaluate objectives simultaneously, and relatively little research has addresse...