Projects are commonly over budget and behind schedule, to some extent because uncertainties are not accounted for in cost and schedule estimates. Research and practice is now addressing this problem, often by using Monte Carlo methods to simulate the effect of variances in work package costs and...

Probabilistic Risk Assessment is being increasingly used in a number of industries such as nuclear, aerospace, chemical process, to name a few. Probabilistic Risk Assessment (PRA) characterizes risk in terms of three questions: (1) What can go wrong? (2) How likely is it? (3) What are the...

Providing a defensible basis for allocating resources for critical infrastructure and key resource protection is an important and challenging problem. Investments can be made in countermeasures that improve the security and hardness of a potential target exposed to a security hazard, deterrence...

Adopting advanced information technologies within the present broad application fields requires precise security. However, security problems regarding information privacy have occurred frequently over the last 5 years despite the contribution of these technologies. To respond to the need for...

Magistriniame darbe nagrinėti ir susisteminti Lietuvos ir užsienio autorių finansinės analizės teoriniai bei praktiniai tyrimai, taip pat aprašytas ir pateiktas finansinės būklės optimizavimo modelis. Analizuojant įmonės veiklą būtina tinkamai parinkti finansinės analizės metodus,...

The primary objective of this study is to propose an estimator of the parameters of the generalized Gaussian noise model with desirable asymptotic properties, namely, asymptotic consistency and asymptotic efficiency. Three estimators are proposed and analyzed. The relative merits and demerits of...

This thesis introduces a new family of classifiers called dyadic decision trees (DDTs) and develops their theoretical properties within the framework of statistical learning theory.First, we show that DDTs achieve optimal rates of convergence for a broad range of classification problems and are...

Source: Dissertation Abstracts International, Volume: 60-07, Section: A, page: 2569.

Bayesian nonparametric methods are useful for modeling data without having to define the complexity of the entire model a priori, but rather allowing for this complexity to be determined by the data. Two problems considered in this dissertation are the number of components in a mixture model,...

Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally efficient methods for Bayesian inference for simultaneous estimation of latent...