There is no unique extension of the standard notion of probabilistic independence to the
case where probabilities are indeterminate or imprecisely specified. Epistemic …

Dynamic Bayesian networks (DBN) are handy tools to model complex dynamical systems
learned from collected data and expert knowledge. However, expert knowledge may be …

Modeling complex dynamical systems from heterogeneous pieces of knowledge varying in
precision and reliability is a challenging task. We propose the combination of dynamical …

We present an efficient algorithm for estimating hidden state sequences in imprecise hidden
Markov models (iHMMs), based on observed output sequences. The main difference with …

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

We present a method for learning imprecise local uncertainty models in stationary hidden
Markov models. If there is enough data to justify precise local uncertainty models, then …

We summarise and provide pointers to recent advances in inference and identification for
specific types of probabilistic graphical models using imprecise probabilities. Robust …

On the Robustness of Imprecise Probability Methods Page 1 On the Robustness of Imprecise
Probability Methods Marco Cattaneo Department of Statistics, LMU Munich ISIPTA ’13 …

One of the big challenges for science is co** with uncertainty, omnipresent in modern
societies and of ever increasing complexity. Quantitative modelling of uncertainty is …

Precise hidden Markov model Consider a stationary precise hidden Markov model (HMM)
with 2n variables: n hidden states Xk, taking values xk in a set {1,..., m} and n observations …