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At least 1,567 records · Page 87Linked to original sources
PubMed2005

Bayesian networks and probabilistic reasoning about scientific evidence when there is a lack of data.

Bayesian networks (BNs) are a kind of graphical model that formally combines elements of graph and probability theory. BNs are a mathematically and statistically rigorous technique allowing their user to define a pictorial representation of assumed dependencies and influences among a set of variables deemed to be relevant for a particular inferential problem. The formalism allows one to process newly acquired evidence according to the rules of probability calculus. Applications of BNs have been reported in various forensic disciplines. However, there seems to be some reluctance to consider BNs as a more general framework for representing and evaluating sources of uncertainties associated with scientific evidence. Notably, BNs are widely thought of as an essentially numerical method, requiring "exact" numbers with a high "accuracy". The present paper aims to draw the reader's attention to the point that the availability of hard numerical data is not a necessary requirement for using BNs in forensic science. An abstraction of quantitative BNs, known as qualitative probabilistic networks (QPNs), and sensitivity analyses are presented and their potential applications discussed. As a main difference to their quantitative counterpart, QPNs contain qualitative probabilistic relationships instead of numerical relations. Sensitivity analyses consist of varying the probabilities assigned to one or more variables and evaluating the effect on one or more other variables of interest. Both QPNs and sensitivity analyses appear to be useful concepts that permit one to work in contexts with acute lack of numerical data and where reasoning consistent with the laws of probability should nevertheless be performed.

Journal Article↗
PubMed1998

Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells.

Fluctuations in rates of gene expression can produce highly erratic time patterns of protein production in individual cells and wide diversity in instantaneous protein concentrations across cell populations. When two independently produced regulatory proteins acting at low cellular concentrations competitively control a switch point in a pathway, stochastic variations in their concentrations can produce probabilistic pathway selection, so that an initially homogeneous cell population partitions into distinct phenotypic subpopulations. Many pathogenic organisms, for example, use this mechanism to randomly switch surface features to evade host responses. This coupling between molecular-level fluctuations and macroscopic phenotype selection is analyzed using the phage lambda lysis-lysogeny decision circuit as a model system. The fraction of infected cells selecting the lysogenic pathway at different phage:cell ratios, predicted using a molecular-level stochastic kinetic model of the genetic regulatory circuit, is consistent with experimental observations. The kinetic model of the decision circuit uses the stochastic formulation of chemical kinetics, stochastic mechanisms of gene expression, and a statistical-thermodynamic model of promoter regulation. Conventional deterministic kinetics cannot be used to predict statistics of regulatory systems that produce probabilistic outcomes. Rather, a stochastic kinetic analysis must be used to predict statistics of regulatory outcomes for such stochastically regulated systems.

Bacteriophage lambda↗
PubMed2004

Uncertainty and the value of diagnostic information, with application to axillary lymph node dissection in breast cancer.

In clinical decision making, it is common to ask whether, and how much, a diagnostic procedure is contributing to subsequent treatment decisions. Statistically, quantification of the value of the information provided by a diagnostic procedure can be carried out using decision trees with multiple decision points, representing both the diagnostic test and the subsequent treatments that may depend on the test's results. This article investigates probabilistic sensitivity analysis approaches for exploring and communicating parameter uncertainty in such decision trees. Complexities arise because uncertainty about a model's inputs determines uncertainty about optimal decisions at all decision nodes of a tree. We present the expected utility solution strategy for multistage decision problems in the presence of uncertainty on input parameters, propose a set of graphical displays and summarization tools for probabilistic sensitivity analysis in multistage decision trees, and provide an application to axillary lymph node dissection in breast cancer.

Axilla↗
PubMed1987

A neural model theory leading to kindling effect of epilepsy.

The dynamics of neural nets constructed of discrete populations of formal neurons were investigated, beginning with the study of single probabilistic nets which were called netlets. The dynamics of these netlets was extended to include steady or slowly varying excitatory or inhibitory inputs. Results obtained with this approach showed simple hysteresis phenomena. However, by considering that the neural connections can be set up by means of chemical markers carried by the individual neurons the dynamics of such systems exhibits not only simple hysteresis but also multiple phenomena. Such hysteresis loops may be considered to represent the basis for short-term memory. The later study of probabilistic neural nets with chemical markers was generalized by considering the intrinsic noise of the system, caused by the spontaneous release of synaptic transmitter substance. On the basis of these studies of noisy neural nets we proposed a model for epileptic phenomena and a theory leading to kindling effect of epilepsy.

Kindling, Neurologic↗
PubMedDate not supplied

Contaminant transport models under random sources.

While the discussion of model uncertainty has centered on spatial heterogeneity, it is possible that ground water models have not enjoyed much success as predictive tools often because the sources that were eventually imposed in the field differed from those represented in the simulations. This is because deterministic prediction of future conditions is often inaccurate due to the random nature of contaminant sources, in terms of their timing, location, and magnitude. This paper presents a stochastic framework for accommodating random contaminant sources in conventional, deterministic advection-dispersion transport models. The contaminant sources are first classified into two types: those occurring continuously with a deterministic component and random variations and those occurring randomly at instantaneous discrete-time intervals. For the first type, the governing partial differential equation (PDE) is replaced by a stochastic PDE. The random variations are modeled by Gaussian noise or Brownian motion, and the solution is obtained by using Ito's integration technique. For the second type, Markovian analysis is used for discrete-time contamination events. Both approaches use a deterministic transport model to generate response functions at any observation location and time. The response functions are then integrated to yield probabilistic description of contaminant transport, from which key statistical properties such as mean, standard deviation, and confidence interval can be drawn.

Forecasting↗
PubMed1988

A mathematical model of recombinational amplification of the 2 mu plasmid in the yeast Saccharomyces cerevisiae.

A mathematical model of 2 mu plasmid recombinational amplification in Saccharomyces cerevisiae has been developed, based on mechanisms of 2 mu recombination and replication presented in the literature. A probabilistic description reveals the limits inherent in the recombinational mode of plasmid amplification. These limits correspond well with values calculated from reported results. In the model, copy number control is effected by the constitutive expression of a repressor of recombinase expression. Estimation of the model parameters is accomplished via a set of heuristic rules which restrict the feasible parameter space considerably. It is demonstrated that many parameter sets arbitrarily chosen from the feasible parameter space reproduce the observed characteristics of 2 mu plasmid amplification: rapid correction of downward copy number deviations, with a lack of strict control of steady-state copy number.

DNA Replication↗
PubMed2005

Probabilistic analysis of the influence of the bonding degree of the stem-cement interface in the performance of cemented hip prostheses.

The long-term behavior of the stem-cement interface is one of the most frequent topics of discussion in the design of cemented total hip replacements, especially with regards to the process of damage accumulation in the cement layer. This effect is analyzed here comparing two different situations of the interface: completely bonded and debonded with friction. This comparative analysis is performed using a probabilistic computational approach that considers the variability and uncertainty of determinant factors that directly compromise the damage accumulation in the cement mantle. This stochastic technique is based on the combination of probabilistic finite elements (PFEM) and a cumulative damage approach known as B-model. Three random variables were considered: muscle and joint contact forces at the hip (both for walking and stair climbing), cement damage and fatigue properties of the cement. The results predicted that the regions with higher failure probability in the bulk cement are completely different depending on the stem-cement interface characteristics. In a bonded interface, critical sites appeared at the distal and medial parts of the cement, while for debonded interfaces, the critical regions were found distally and proximally. In bonded interfaces, the failure probability was higher than in debonded ones. The same conclusion may be established for stair climbing in comparison with walking activity.

Cementation↗
PubMed2005

Spatio-temporal free-form registration of cardiac MR image sequences.

In this paper we present two registration algorithms for the spatio-temporal alignment of cardiac MR image sequences. Both algorithms have the ability to correct spatial misalignment between the images sequences caused by global and local shape differences. In addition, they have the ability to correct temporal misalignment caused by differences in the length of the cardiac cycles and by differences in the dynamic properties of the hearts. The algorithms use a 4D deformable transformation model which is separated into spatial and temporal components. The first registration algorithm optimizes the spatial and temporal transformation models simultaneously, while the second registration algorithm optimizes the temporal transformation component before optimizing the spatial component. For the evaluation of the spatio-temporal registration methods we have acquired 15 MR image sequences from healthy volunteers. The registration methods were quantitatively evaluated by measuring the overlap and surface distance of anatomical regions and qualitatively by visual inspection. The results demonstrate that a significant improvement in the alignment of the image sequences is achieved by the use of the deformable spatio-temporal transformation model. We demonstrate the use of the method for the construction of a probabilistic MR cardiac atlas representing the anatomy and function of a healthy heart.

Algorithms↗
PubMed1998

Cosmological implications of a large complete quasar sample.

Objective and reproducible determinations of the probabilistic significance levels of the deviations between theoretical cosmological prediction and direct model-independent observation are made for the Large Bright Quasar Sample [Foltz, C., Chaffee, F. H., Hewett, P. C., MacAlpine, G. M., Turnshek, D. A., et al. (1987) Astron. J. 94, 1423-1460]. The Expanding Universe model as represented by the Friedman-Lemaitre cosmology with parameters qo = 0, Lambda = 0 denoted as C1 and chronometric cosmology (no relevant adjustable parameters) denoted as C2 are the cosmologies considered. The mean and the dispersion of the apparent magnitudes and the slope of the apparent magnitude-redshift relation are the directly observed statistics predicted. The C1 predictions of these cosmology-independent quantities are deviant by as much as 11sigma from direct observation; none of the C2 predictions deviate by >2sigma. The C1 deviations may be reconciled with theory by the hypothesis of quasar "evolution," which, however, appears incapable of being substantiated through direct observation. The excellent quantitative agreement of the C1 deviations with those predicted by C2 without adjustable parameters for the results of analysis predicated on C1 indicates that the evolution hypothesis may well be a theoretical artifact.

Journal Article↗
PubMed2003

Deciding between accounts of the selection task: a reply to Oaksford (2002).

In this paper we report on our attempts to fit the optimal data selection (ODS) model (Oaksford & Chater, 1994; Oaksford, Chater, & Larkin, 2000) to the selection task data reported in Feeney and Handley (2000) and Handley, Feeney, and Harper (2002). Although Oaksford (2002b) reports good fits to the data described in Feeney and Handley (2000), the model does not adequately capture the data described in Handley et al. (2002). Furthermore, across all six of the experiments modelled here, the ODS model does not predict participants' behaviour at the level of selection rates for individual cards. Finally, when people's probability estimates are used in the modelling exercise, the model adequately captures only 1 out of 18 conditions described in Handley et al. We discuss the implications of these results for models of the selection task and claim that they support deductive, rather than probabilistic, accounts of the task.

Choice Behavior↗
PubMed2000

Background estimation in experimental spectra

A general probabilistic technique for estimating background contributions to measured spectra is presented. A Bayesian model is used to capture the defining characteristics of the problem, namely, that the background is smoother than the signal. The signal is allowed to have positive and/or negative components. The background is represented in terms of a cubic spline basis. A variable degree of smoothness of the background is attained by allowing the number of knots and the knot positions to be adaptively chosen on the basis of the data. The fully Bayesian approach taken provides a natural way to handle knot adaptivity and allows uncertainties in the background to be estimated. Our technique is demonstrated on a particle induced x-ray emission spectrum from a geological sample and an Auger spectrum from iron, which contains signals with both positive and negative components.

Journal Article↗
PubMed2004

A Bayesian morphometry algorithm.

Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper, we propose Bayesian morphological analysis methods, based on a Bayesian-network representation, for the analysis of MR brain images. First, we describe how Bayesian networks (BNs) can represent probabilistic associations among voxels and clinical (function) variables. Second, we present a model-selection framework, which generates a BN that captures structure-function relationships from MR brain images and function variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structure-function association is nonlinear. Our methods successfully identify voxel-wise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i.e., t-test and paired t-test) fails in the nonlinear-association case.

Aged↗
PubMed2006

An incremental training method for the probabilistic RBF network.

The probabilistic radial basis function (PRBF) network constitutes a probabilistic version of the RBF network for classification that extends the typical mixture model approach to classification by allowing the sharing of mixture components among all classes. The typical learning method of PRBF for a classification task employs the expectation-maximization (EM) algorithm and depends strongly on the initial parameter values. In this paper, we propose a technique for incremental training of the PRBF network for classification. The proposed algorithm starts with a single component and incrementally adds more components at appropriate positions in the data space. The addition of a new component is based on criteria for detecting a region in the data space that is crucial for the classification task. After the addition of all components, the algorithm splits every component of the network into subcomponents, each one corresponding to a different class. Experimental results using several well-known classification data sets indicate that the incremental method provides solutions of superior classification performance compared to the hierarchical PRBF training method. We also conducted comparative experiments with the support vector machines method and present the obtained results along with a qualitative comparison of the two approaches.

Algorithms↗
PubMed2006

Genetically engineered plants, endangered species, and risk: a temporal and spatial exposure assessment for Karner blue butterfly larvae and Bt maize pollen.

Genetically engineered maize (Zea mays) containing insecticidal endotoxin proteins from Bacillus thuringiensis (Bt) delta-endotoxin proteins has been adopted widely in the Midwestern United States. The proteins are toxic to several lepidopteran species and because a variety of maize tissues, including pollen, may express the endotoxins, the probability of exposure to nontarget species, including endangered species, needs to be understood. The objective of this study was to assess the potential temporal and spatial exposure of endangered Karner blue butterfly larvae (Lycaeides melissa samuelis) to Bt maize pollen in Wisconsin using probabilistic exposure techniques and geographic information systems analysis. Based on degree-day modeling of butterfly phenology and maize pollen shed, there is some potential for temporal exposure of larvae to maize pollen. However, in the majority of years and locations, maize pollen shed most likely will occur after the majority of larval feeding on wild lupine (Lupinus perennis). The spatial analysis indicates that some Karner blue butterfly populations occur in close proximity to maize fields, but in the vast majority of cases the butterfly's host plant and maize fields are separated by more than 500 m. A small number of potential or existing Karner blue butterfly sites are located near maize fields, including sites in two of the four counties where temporal overlap is most likely. The exposure assessment indicates that these two counties should receive the highest priority to determine if Karner blue butterfly larvae are actually at risk and then, if needed, to reduce or prevent exposure.

Animals↗
PubMed2003

Reduced models for the medium-frequency dynamics of stochastic systems.

In this paper, a frequency domain vibration analysis procedure of a randomly parametered structural system is described for the medium-frequency range. In this frequency range, both traditional modal analysis and statistical energy analysis (SEA) procedures well-suited for low- and high-frequency vibration analysis respectively, lead to computational and conceptual difficulties. The uncertainty in the structural system can be attributed to various reasons such as the coupling of the primary structure with a variety of secondary systems for which conventional modeling is not practical. The methodology presented in the paper consists of coupling probabilistic reduction methods with dynamical reduction methods. In particular, the Karhunen-Loeve and Polynomial Chaos decompositions of stochastic processes are coupled with an operator decomposition scheme based on the spectrum of an energy operator adapted to the frequency band of interest.

Journal Article↗
PubMedDate not supplied

[Use of probabilistic methods in economic evaluation of health technologies].

The present article illustrates the advantages of probabilistic methods in health technology assessment. Using probabilistic distributions for parameters, these methods represent parameter uncertainty more effectively than deterministic models. The results of this simulation process represent the joint distribution of cost and effects in the cost-effective plane. More precisely, the cost-effectiveness ratio of each simulation is used to determine whether the ratio is cost effective in relation to the ceiling ratio. This kind of information is represented in the cost-effectiveness acceptability curve, which indicates the probability of a health technology being cost-effective for a particular quality-adjusted life year value.

Biomedical Technology↗
PubMedDate not supplied

Evaluation of nonlinear optimization for scheduling of follow-up cystoscopies to detect recurrent bladder cancer. The Bladder Cancer follow-up Group.

Standard recommendations for patients who have had superficial bladder cancer are inspection by cystoscopy quarterly for a year or two after tumor removal, then half-yearly and yearly. The authors assessed the potential for improvement in scheduling cystoscopies according to probabilistic optimization techniques. Eight hypothetical practices were created, based on retrospective analysis of 918 bladder-cancer-patient charts. Standard and alternative recommendations for the interval to next cystoscopy were compared. The alternatives were derived from patient-specific predictions of future tumor risks (based on the patient's prior recurrence rate and tumor stage and grade) and a nonlinear optimization approach to allocation of the same number of cystoscopies as were available for standard follow-up. The optimization proposed longer intervals between visits for low-risk patients and shorter intervals for high-risk patients. Overall, optimization reduced expected tumor detection delays by 30%, from 12.6 to 8.7 weeks. When optimization intervals were shorter than standard, cancer was found more often at subsequent cystoscopies (34% vs 27%, p less than 0.05), suggesting that the optimization was a better predictor of cancer recurrence. If reduction in tumor-detection delay is the goal of follow-up for recurrent cancers, then urologists can improve monitoring by using probabilistic optimization methods for scheduling cystoscopies. Further understanding of the accuracy of predictive models for bladder-cancer recurrence rates is desirable. Subsequently, the optimization method developed here may be tested prospectively.

Aftercare↗
PubMed2006

The generalisability of pharmacoeconomic studies: issues and challenges ahead.

Developing from a previous review, this article revisits the generalisability theme to summarise recent advances in methodology and provide an update of challenges faced by producers and users of pharmacoeconomic data. Our original evaluative criteria encompassed technical issues, applicability and transferability. The technical elements of best practice are comparatively uncontroversial: choosing relevant alternatives; transparent reporting of methods and findings; accessing and applying the best-quality evidence; using best methods to synthesise data; and using deterministic sensitivity analysis to explore potential systematic bias whilst employing probabilistic sensitivity analysis to explore the influence of random error at the whole model level. The applicability of economic findings within their original policy context (e.g. national analyses based on generalisable within-country data) can be determined, provided that best practice guidelines for economic modelling are adhered to. The transferability of economic findings (from one policy setting to another, e.g. country, region, clinical setting or patient population) requires careful exploration of changes in resource implications, unit prices and outcomes, a process facilitated again by transparent reporting of methods, adjustment for baseline risk and potentially by recent statistical developments intended to deal with hierarchically structured data. Although there is considerable consensus in the published literature about these key issues, limitations remain for economic analysis as implemented because of its opaqueness of method, failure to reflect the opportunity cost of decisions and lack of societal mandate. If the primary purpose of health economic evaluation is to help society to obtain the best value from limited resources, then, at a time when most technologically advanced societies need to engage with the realities of limited healthcare funding, technocratic solutions alone appear insufficient. Making health economic findings accessible to patients, clinicians and society, in the form of relevant narratives, will help this essential debate and expose assumptions underpinning economic analysis to broader critical inspection.

Economics, Pharmaceutical↗