A Bayesian model for estimating the malaria transition probabilities considering individuals lost to follow-up

It is known that patients may cease participating in a longitudinal study and become lost to follow-up. The objective of this article is to present a Bayesian model to estimate the malaria transition probabilities considering individuals lost to follow-up. We consider a homogeneous population, and it is assumed that the considered period of time is small enough to avoid two or more transitions from one state of health to another. The proposed model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of the longitudinal study. To simulate the unknown number of individuals with positive and negative states of malaria at the end of the study and lost to follow-up, two latent variables were introduced in the model. We used a real data set and a simulated data to illustrate the application of the methodology. The proposed model showed a good fit to these data sets, and the algorithm did not show problems of convergence or lack of identifiability. We conclude that the proposed model is a good alternative to estimate probabilities of transitions from one state of health to the other in studies with low adherence to follow-up.

Efficient solutions to factored MDPs with imprecise transition probabilities

When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural uncertainty arises in the transition specification due to elicitation of MOP transition models from an expert or estimation from data, or non-stationary transition distributions arising from insufficient state knowledge. In the interest of obtaining the most robust policy under transition uncertainty, the Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) has been introduced to model such scenarios. Unfortunately, while various solution algorithms exist for MDP-IPs, they often require external calls to optimization routines and thus can be extremely time-consuming in practice. To address this deficiency, we introduce the factored MDP-IP and propose efficient dynamic programming methods to exploit its structure. Noting that the key computational bottleneck in the solution of factored MDP-IPs is the need to repeatedly solve nonlinear constrained optimization problems, we show how to target approximation techniques to drastically reduce the computational overhead of the nonlinear solver while producing bounded, approximately optimal solutions. Our results show up to two orders of magnitude speedup in comparison to traditional ""flat"" dynamic programming approaches and up to an order of magnitude speedup over the extension of factored MDP approximate value iteration techniques to MDP-IPs while producing the lowest error of any approximation algorithm evaluated. (C) 2011 Elsevier B.V. All rights reserved.

Excited Electronic States, Transition Probabilities, and Radiative Lifetimes of CAs: A Theoretical Contribution and Challenge to Experimentalists

High-level CASSCF/MRCI calculations with a quintuple-zeta quality basis set are reported by characterizing for the first time a manifold of electronic states of the CAs radical yet to be investigated experimentally. Along with the potential energy curves and the associated spectroscopic constants, the dipole moment functions for selected electronic states as well as the transition dipole moment functions for the most relevant electronic transitions are also presented. Estimates of radiative transition probabilities and lifetimes complement this investigation, which also assesses the effect of spin-orbit interaction on the A (2)Pi state. Whenever pertinent, comparisons of similarities and differences with the isovalent CN and CP radicals are made.

Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities

Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem.The algorithm consists in adding a fuzzy logic controller to control and tunedynamically different parameters (probabilities of crossover and mutation), in anattempt to improve the algorithm performance. For this purpose, we will design afuzzy logic controller based on fuzzy rules to control the probabilities of crossoverand mutation. Compared with the Standard Genetic Algorithm (SGA), the resultsclearly demonstrate that the FLGA method performs significantly better.

A model for compressing probabilities in belief networks

Probabilistic reasoning with belief (Bayesian) networks is based on conditional probability matrices. Thus it suffers from NP-hard implementations. In particular, the amount of probabilistic information necessary for the computations is often overwhelming. So, compressing the conditional probability table is one of the most important issues faced by the probabilistic reasoning community. Santos suggested an approach (called linear potential functions) for compressing the information from a combinatorial amount to roughly linear in the number of random variable assignments. However, much of the information in Bayesian networks, in which there are no linear potential functions, would be fitted by polynomial approximating functions rather than by reluctantly linear functions. For this reason, we construct a polynomial method to compress the conditional probability table in this paper. We evaluated the proposed technique, and our experimental results demonstrate that the approach is efficient and promising.

On Qualitative Probabilities for Legal Reasoning about Evidence

A crucial aspect of evidential reasoning in crime investigation involves comparing the support that evidence provides for alternative hypotheses. Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be employed for this purpose. However, the specification of BNs requires conditional probability tables describing the uncertain processes under evaluation. When these processes are poorly understood, it is necessary to rely on subjective probabilities provided by experts. Accurate probabilities of this type are normally hard to acquire from experts. Recent work in qualitative reasoning has developed methods to perform probabilistic reasoning using coarser representations. However, the latter types of approaches are too imprecise to compare the likelihood of alternative hypotheses. This paper examines this shortcoming of the qualitative approaches when applied to the aforementioned problem, and identifies and integrates techniques to refine them.

A Complexity Based Model for Quantifying Forensic Evidential Probabilities

An operational complexity model (OCM) is proposed to enable the complexity of both the cognitive and the computational components of a process to be determined. From the complexity of formation of a set of traces via a specified route a measure of the probability of that route can be determined. By determining the complexities of alternative routes leading to the formation of the same set of traces, the odds ratio indicating the relative plausibility of the alternative routes can be found. An illustrative application to a BitTorrent piracy case is presented, and the results obtained suggest that the OCM is capable of providing a realistic estimate of the odds ratio for two competing hypotheses. It is also demonstrated that the OCM can be straightforwardly refined to encompass a variety of circumstances.

Activities between activities of focus-Relevant when assessing DNA transfer probabilities

Consideration of the indirect transfer of 'touch DNA' is increasingly becoming part of criminal investigations. Focus is often concentrated on the actions relating to the pick-up of the relevant DNA and key actions associated with transfer to the exhibit from which the sample in question was collected. There is often a time lapse between such actions. As any contact can influence the gain and/or loss of DNA, it is relevant to have an awareness of what hands touch during everyday activities in order to assist consideration of what may be occurring during potential time lapses within contemplated scenarios. To gain an appreciation of the manner and frequency of hands contacting various surfaces during everyday activities, we analysed several videos of individuals performing a variety of general activities. The findings indicate that several items are touched over a relatively short period of time. Appreciation and consideration of general activities that may have occurred between key focus activities are necessary to assess any impact these may have on what is deposited at the final collection site. The information this provides is imperative when weighting alternative transfer scenario propositions.

Laws of large numbers for non-additive probabilities

We apply the concept of exchangeable random variables to the case of non-additive robability distributions exhibiting ncertainty aversion, and in the lass generated bya convex core convex non-additive probabilities, ith a convex core). We are able to rove two versions of the law of arge numbers (de Finetti's heorems). By making use of two efinitions. of independence we rove two versions of the strong law f large numbers. It turns out that e cannot assure the convergence of he sample averages to a constant. e then modal the case there is a true" probability distribution ehind the successive realizations of the uncertain random variable. In this case convergence occurs. This result is important because it renders true the intuition that it is possible "to learn" the "true" additive distribution behind an uncertain event if one repeatedly observes it (a sufficiently large number of times). We also provide a conjecture regarding the "Iearning" (or updating) process above, and prove a partia I result for the case of Dempster-Shafer updating rule and binomial trials.

Optical transition probabilities and compositional dependence of Judd-Ofelt parameters of Er3+ ions in fluoroindate glass

Fluoroindate glasses containing 1, 2, 3, and 4 mol% ErF3 were prepared in a dry box under an argon atmosphere. Absorption spectra of these glasses at room temperature were obtained. The Judd-Ofelt parameters Ωλ (λ = 2, 4, 6) for f-f transitions of Er3+ ions as well as transition probabilities, branching ratios, radiative lifetimes, and peak cross-sections for stimulated emission of each band were determined. The concentration effect on the intensities is analyzed. The optical properties of the fluoroindate glasses doped with Er3+ ions are compared with those of other glasses described in the literature. © 1995.

Detecting migrants in populations of Rhizoctonia solani anastomosis group 3 from potato in North Carolina using multilocus genotype probabilities

The relative contribution of migration of Rhizoctonia solani anastomosis group 3 (AG-3) on infested potato seed tubers originating from production areas in Canada, Maine, and Wisconsin (source population) to the genetic diversity and structure of populations of R. solani AG-3 in North Carolina (NC) soil (recipient population) was examined. The frequency of alleles detected by multilocus polymerase chain reaction-restriction fragment length polymorphisms, heterozygosity at individual loci, and gametic phase disequilibrium between all pairs of loci were determined for subpopulations of R. solani AG-3 from eight sources of potato seed tubers and from five soils in NC. Analysis of molecular variation revealed little variation between seed source and NC recipient soil populations or between subpopulations within each region. Analysis of population data with a Bayesian-based statistical method previously developed for detecting migration in human populations suggested that six multilocus genotypes from the NC soil population had a statistically significant probability of being migrants from the northern source population. The one-way (unidirectional) migration of genotypes of R. solani AG-3 into NC on infested potato seed tubers from Canada, Maine, and Wisconsin provides a plausible explanation for the lack of genetic subdivision (differentiation) between populations of the pathogen in NC soils or between the northern source and the NC recipient soil populations.

Average control of Markov decision processes with Feller transition probabilities and general action spaces

This paper studies the average control problem of discrete-time Markov Decision Processes (MDPs for short) with general state space, Feller transition probabilities, and possibly non-compact control constraint sets A(x). Two hypotheses are considered: either the cost function c is strictly unbounded or the multifunctions A(r)(x) = {a is an element of A(x) : c(x, a) <= r} are upper-semicontinuous and compact-valued for each real r. For these two cases we provide new results for the existence of a solution to the average-cost optimality equality and inequality using the vanishing discount approach. We also study the convergence of the policy iteration approach under these conditions. It should be pointed out that we do not make any assumptions regarding the convergence and the continuity of the limit function generated by the sequence of relative difference of the alpha-discounted value functions and the Poisson equations as often encountered in the literature. (C) 2012 Elsevier Inc. All rights reserved.