The ever-happening disruptive events interrupt the operationalisation of manufacturing
organisations resulting in stalling the production flow and depleting societies with products …

A wide range of quantitative and qualitative modelling research on ecosystem services
(ESS) has recently been conducted. The available models range between elementary …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The purpose of this paper is to provide a decision support system for managers to predict an
organization's probability of successful blockchain adoption using a machine learning …

Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …

Winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that
support the modelling of uncertainty and decisions in large complex domains, while …

Since the first edition of this book published, Bayesian networks have become even more
important for applications in a vast array of fields. This second edition includes new material …

Abstract Machine-learning research has been making great progress in many directions.
This article summarizes four of these directions and discusses some current open problems …

Formal ways of representing uncertainty and various logics for reasoning about it; updated
with new material on weighted probability measures, complexity-theoretic considerations …