Predicting Remaining Useful Life with Similarity-Based Priors

Prognostics is the area of research that is concerned with predicting the remaining useful life of machines and machine parts. The remaining useful life is the time during which a machine or part can be used, before it must be replaced or repaired. To create accurate predictions, predictive techniques must take external data into account on the operating conditions of the part and events that occurred during its lifetime. However, such data is often not available. Similarity-based techniques can help in such cases. They are based on the hypothesis that if a curve developed similarly to other curves up to a point, it will probably continue to do so. This paper presents a novel technique for similarity-based remaining useful life prediction. In particular, it combines Bayesian updating with priors that are based on similarity estimation. The paper shows that this technique outperforms other techniques on long-term predictions by a large margin, although other techniques still perform better on short-term predictions.</p

Bayesian modeling of networks in complex business intelligence problems

Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer choices of specific products, along with co-subscription networks encoding multiple purchasing behavior. Data are available for several agencies within the same insurance company, and our goal is to efficiently exploit co-subscription networks to inform targeted advertising of cross-sell strategies to currently mono-product customers. We address this goal by developing a Bayesian hierarchical model, which clusters agencies according to common mono-product customer choices and co-subscription networks. Within each cluster, we efficiently model customer behavior via a cluster-dependent mixture of latent eigenmodels. This formulation provides key information on mono-product customer choices and multiple purchasing behavior within each cluster, informing targeted cross-sell strategies. We develop simple algorithms for tractable inference, and assess performance in simulations and an application to business intelligence

Criminal History Enhancements Sourcebook

Criminal history scores make up one of the two most significant determinants of the punishment an offender receives in a sentencing guidelines jurisdiction. While prior convictions are taken into account by all U.S. sentencing systems, sentencing guidelines make the role of prior crimes more explicit by specifying the counting rules and by indicating the effect of prior convictions on sentence severity. Yet, once established, criminal history scoring formulas go largely unexamined. Moreover, there is great diversity across state and federal jurisdictions in the ways that an offender's criminal record is considered by courts at sentencing. This Sourcebook brings together for the first time information on criminal history enhancements in all existing U.S. sentencing guidelines systems. Building on this base, the Sourcebook examines major variations in the approaches taken by these systems, and identifies the underlying sentencing policy issues raised by such enhancements.The Sourcebook contains the following elements:A summary of criminal history enhancements in all guidelines jurisdictions;An analysis of the critical dimensions of an offender's previous convictions;A discussion of the policy options available to commissions considering amendments to their criminal history enhancements;A bibliography of key readings on the role of prior convictions at sentencing