Probabilistic diagnostics with P-graphs

This paper presents a novel approach for solving the probabilistic diagnosis problem in multiprocessor systems. The main idea of the algorithm is based on the reformulation of the diagnostic procedure as a P-graph model. The same, well-elaborated mathematical paradigm - originally used to model material flow - can be applied in our approach to model information flow. This idea is illustrated by deriving a maximum likelihood diagnostic decision procedure. The diagnostic accuracy of the solution is considered on the basis of simulation measurements, and a method of constructing a general framework for different aspects of a complex problem is demonstrated with the use of P-graph models

An R Package for a General Class of Inverse Gaussian Distributions

The inverse Gaussian distribution is a positively skewed probability model that has received great attention in the last 20 years. Recently, a family that generalizes this model called inverse Gaussian type distributions has been developed. The new R package named ig has been designed to analyze data from inverse Gaussian type distributions. This package contains basic probabilistic functions, lifetime indicators and a random number generator from this model. Also, parameter estimates and diagnostics analysis can be obtained using likelihood methods by means of this package. In addition, goodness-of-fit methods are implemented in order to detect the suitability of the model to the data. The capabilities and features of the ig package are illustrated using simulated and real data sets. Furthermore, some new results related to the inverse Gaussian type distribution are also obtained. Moreover, a simulation study is conducted for evaluating the estimation method implemented in the ig package.

Calibration and Resolution Diagnostics for Bank of England Density Forecasts

This paper applies new diagnostics to the Bank of England’s pioneering density forecasts (fan charts). We compute their implicit probability forecast for annual rates of inflation and output growth that exceed a given threshold (in this case, the target inflation rate and 2.5% respectively.) Unlike earlier work on these forecasts, we measure both their calibration and their resolution, providing both formal tests and graphical interpretations of the results. These results both reinforce earlier evidence on some of the limitations of these forecasts and provide new evidence on their information content. Cet étude développe et applique des nouvelles techniques pour diagnostiquer les prévisions de densité de la Banque d’Angleterre (leur “fan charts”). Nous calculons leurs probabilités implicites pour des taux d’inflation et de croissance du PIB qui dépassent des seuils critiques (soit le taux d’inflation ciblé, soit 2.5%.) En contraste avec des travaux antérieurs sur ces prévisions, nous gaugeons leur calibration aussi bien que leur résolution, en donnant des tests formels et des interprétations graphiques. Les résultats renforcent des conclusions déjà existant sur les limites de ces prévisions et ils donnent de nouvelles évidences sur leurs valeurs ajoutées.calibration, density forecast, probability forecast, resolu, calibration, prévisions de densité, probabilités implicites, résolution.

Towards a Multi-Subject Analysis of Neural Connectivity

Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected from multiple subjects whose connectivities may differ but are likely to share many features. In such circumstances it is natural to leverage similarity between subjects to improve statistical efficiency. The first exact algorithm for estimation of multiple related DAGs was recently proposed by Oates et al. 2014; in this letter we present examples and discuss implications of the methodology as applied to the analysis of fMRI data from a multi-subject experiment. Elicitation of tuning parameters requires care and we illustrate how this may proceed retrospectively based on technical replicate data. In addition to joint learning of subject-specific connectivity, we allow for heterogeneous collections of subjects and simultaneously estimate relationships between the subjects themselves. This letter aims to highlight the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2

Learning Bayesian Networks with the bnlearn R Package

bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package.Comment: 22 pages, 4 picture