Credal networks generalize Bayesian networks to allow for imprecision in probability values.
This paper reviews the main results on credal networks under strong independence, as …

A novel technique to classify time series with imprecise hidden Markov models is presented.
The learning of these models is achieved by coupling the EM algorithm with the imprecise …

Credal networks are graph-based statistical models whose parameters take values in a set,
instead of being sharply specified as in traditional statistical models (eg, Bayesian …

We present a new approach to credal networks, which are graphical models that generalise
Bayesian networks to deal with imprecise probabilities. Instead of applying the commonly …

This chapter discusses defining a model over its whole set of variables by the composition of
a number of sub‐models each involving only fewer variables. It focuses on the kind of …

At the core of Bayesian probability theory, or dually desirability theory, lies an assumption of
linearity of the scale in which rewards are measured. We revisit two recent papers that …

We present an efficient exact algorithm for estimating state sequences from outputs or
observations in imprecise hidden Markov models (iHMMs). The uncertainty linking one state …

Abstract Hidden Markov models (HMMs) are widely used probabilistic models of sequential
data. As with other probabilistic models, they require the specification of local conditional …

Kuznetsov independence of variables X and Y means that, for any pair of bounded functions
f (X) and g (Y), E [f (X) g (Y)]= E [f (X)]⊠ E [g (Y)], where E [⋅] denotes interval-valued …

This article discusses the current state of the art in terms of computational complexity for the
problem of finding the most probable configuration of a subset of variables in a multivariate …