A note on the temporal sure preference principle and the updating of lower previsions.

This paper reviews the temporal sure preference principle as a basis for inference over time. We reformulate the principle in terms of desirability, and explore its implications for lower previsions. We report some initial results. We also discuss some of the technical difficulties encountered

Logistic regression on Markov chains for crop rotation modelling.

Often, in dynamical systems, such as farmer's crop choices, the dynamics is driven by external non-stationary factors, such as rainfall, temperature, and economy. Such dynamics can be modelled by a non-stationary Markov chain, where the transition probabilities are logistic functions of such external factors. We investigate the problem of estimating the parameters of the logistic model from data, using conjugate analysis with a fairly broad class of priors, to accommodate scarcity of data and lack of strong prior expert opinions. We show how maximum likelihood methods can be used to get bounds on the posterior mode of the parameters

A robust data driven approach to quantifying common-cause failure in power networks.

The standard alpha-factor model for common cause failure assumes symmetry, in that all components must have identical failure rates. In this paper, we generalise the alpha-factor model to deal with asymmetry, in order to apply the model to power networks, which are typically asymmetric. For parameter estimation, we propose a set of conjugate Dirichlet-Gamma priors, and we discuss how posterior bounds can be obtained. Finally, we demonstrate our methodology on a simple yet realistic example

Model checking for imprecise Markov chains.

We extend probabilistic computational tree logic for expressing properties of Markov chains to imprecise Markov chains, and provide an efficient algorithm for model checking of imprecise Markov chains. Thereby, we provide a formal framework to answer a very wide range of questions about imprecise Markov chains, in a systematic and computationally efficient way

A robust Bayesian analysis of the impact of policy decisions on crop rotations.

We analyse the impact of a policy decision on crop rotations, using the imprecise land use model that was developed by the authors in earlier work. A specific challenge in crop rotation models is that farmer’s crop choices are driven by both policy changes and external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary stochastic process, where crop transition probabilities are multinomial logistic functions of such external factors. We use a robust Bayesian approach to estimate the parameters of our model, and validate it by comparing the model response with a non-parametric estimate, as well as by cross validation. Finally, we use the resulting predictions to solve a hypothetical yet realistic policy problem