MOTIVATION In life sciences, interpretability of machine learning models is as important as their prediction accuracy. Linear models are probably the most frequently used methods for assessing feature relevance, despite their relative inflexibility. However, in the past years effective estimators of feature relevance have been derived for highly complex or non-parametric models such as support ...

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Background and objectives: Dental technicians are in indirect contact with patients. Microorganisms find their way to dental labs through dental impressions. Infection control is a critical procedure to prevent the spreading of diseases. The objective of this study was to evaluate the levels of infection control knowledge and practice and the factors affecting them, in DLs in Yazd – Iran. Meth...

Factorisation of probability trees is a useful tool for inference in Bayesian networks. Probabilistic potentials some of whose parts are proportional can be decomposed as a product of smaller trees. Some algorithms, like lazy propagation, can take advantage of this fact. Also, the factorisation can be used as a tool for approximating inference, if the decomposition is carried out even if the pr...

A causal graph G(V, E) specifies causal relationships among the random variables representing the vertices of the graph V . The relationships are specified by the directed edges E ; an edge Vi ! Vj implies that Vi 2 V is a direct parental cause for the effect Vj 2 V . With some abuse of notation, we will denote the random variable associated with a node V 2 V by V itself. We will denote the par...

Annealed importance sampling is a means to assign equilibrium weights to a nonequilibrium sample that was generated by a simulated annealing protocol[1]. The weights may then be used to calculate equilibrium averages, and also serve as an “adiabatic signature” of the chosen cooling schedule. In this paper we demonstrate the method on the 50-atom dileucine peptide, showing that equilibrium distr...