Optimized Bayesian dynamic advising: theory and algorithms

Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modelling by dynamic mixture model

Topics in Mixture Estimation

This report constitutes an unrefereed manuscript which is intended to be submitted for publication. Any opinions and conclusions expressed in this report are those of the author(s) and do not necessarily represent the views of the Institute. Acknowledgement

Decision making and imperfection

Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process.   The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider:   ·          how a crowd of imperfect decision makers outperforms experts' decisions;   ·          how to decrease decision makers' imperfection by reducing knowledge available;   ·          how to decrease imperfection via automated elicitation of DM preferences;   ·          a human's limited willingness to master the available decision-support tools as an additional source of imperfection;   ·          how the decision maker's emotional state influences the rationality;  a DM support of edutainment robot based on its system of values and respecting emotions.   The book will appeal to anyone interested in the challenging topic of DM theory and its applications.

Factorized EM Algorithm for Mixture Estimation

A classical version of the EM algorithm is considered in the paper. Its numerical properties are improved using factorized algorithms for maximization in M step of the algorithm. The results are illustrated on simulated examples

Statistical Decision Making and Neural Networks

This technical proposition specifies conditions under which the "expectation" becomes the ordinary expectation of the losses, "deformed" by a utility function U . U expresses the decision-maker's attitude to the loss values: he might be risk aware, risk prone or risk indifferent. The utility function U and measure ¯ are universal with respect to a rich set of decision tasks facing the same uncertainty

Mixed-Data Multi-Modelling for Fault Detection and Isolation

This paper contributes to a solution of the complexity aspect of FDI. It proposes a quasiBayesian estimation algorithm [8] that combats the "curse of dimensionality" connected with use of the multiple-model approach to FDI [4, 3]. It stems from the fact that the likelihood function needed for parameter estimation is a product of sums of functions of parameters, Consequently, it contains exactly unmanageable number of term