Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “probabilistic modelling”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,459 records · Page 81Linked to original sources
PubMedDate not supplied

Evaluating physicians' probabilistic judgments.

Physicians increasingly are challenged to make probabilistic judgments quantitatively. Their ability to make such judgments may be directly linked to the quality of care they provide. Many methods are available to evaluate these judgments. Graphic means of assessment include the calibration curve, covariance graph, and receiver operating characteristic (ROC) curve. Statistical tools can measure the significance of departures from ideal calibration, and measure the area under ROC curve. Modeling the calibration curve using linear or logistic regression provides another method to assess probabilistic judgments, although these may be limited by failure of the data to meet the model's assumptions. Scoring rules provide indices of overall judgmental performance, although their reliability is difficult to gauge for small sample sizes. Decompositions of scoring rules separate judgmental performance into functional components. The authors provide preliminary guidelines for choosing methods for specific research in this area.

Decision Theory↗
PubMed2001

On the Complexity of Additive Clustering Models.

Additive clustering provides a conceptually simple and potentially powerful approach to modeling the similarity relationships between stimuli. The ability of additive clustering models to accommodate similarity data, however, typically arises through the incorporation of large numbers of parameterized clusters. Accordingly, for the purposes of both model generation and model comparison, it is necessary to develop quantitative evaluative measures of additive clustering models that take into account both data-fit and complexity. Using a previously developed probabilistic formulation of additive clustering, the Bayesian Information Criterion is proposed for this role, and its application demonstrated. Limitations inherent in this approach, including the assumption that model complexity is equivalent to cluster cardinality, are discussed. These limitations are addressed by applying the Laplacian approximation of a marginal probability density, from which a measure of cluster structure complexity is derived. Using this measure, a preliminary investigation is made of the various properties of cluster structures that affect additive clustering model complexity. Among other things, these investigations show that, for a fixed number of clusters, a model with a strictly nested cluster structure is the least complicated, while a model with a partitioning cluster structure is the most complicated. Copyright 2001 Academic Press.

Journal Article↗
PubMed2005

The development of a spatially explicit model to estimate radiocaesium body burdens in raccoons (Procyon lotor) for ecological risk assessment.

A spatially explicit model of raccoon (Procyon lotor) distribution for the U.S. Department of Energy's (DOE) Savannah River Site (SRS) in west-central South Carolina was developed using data from a raccoon radio-telemetry study and visualized within a Geographic Information System (GIS). An inductive approach was employed to develop three sub-models using the ecological requirements of raccoons studied in the following habitats: (1) man-made reservoirs, (2) bottomland hardwood/riverine systems, and (3) isolated wetland systems. Logistic regression was used to derive probabilistic resource selection functions using habitat compositional data and landscape metrics. The final distribution model provides a spatially explicit probability (likelihood of being in an area) surface for male raccoons. The model is a stand-alone tool consisting of algorithms independent of the specific GIS data layers to which they were derived. The model was then used to predict contaminant burdens in raccoons inhabiting a riverine system contaminated with radiocaesium (137Cs). The predicted 137Cs burdens were less than if one would assume homogeneous use of the contaminated areas. This modelling effort provides a template for DOE managed lands and other large government facilities to establish a framework for site-specific ecological assessments that use wildlife species as endpoints.

Animals↗
PubMed2006

Probabilistic fusion of stereo with color and contrast for bilayer segmentation.

This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, contrast, and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color model that is learned on-the-fly and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead, the stereo match likelihood is marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/ contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.

Algorithms↗
PubMed1996

A knowledge-based model construction approach to medical decision making.

We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.

Acute Disease↗
PubMed1990

A bivariate negative binomial model to explain traffic accident migration.

The phenomenon of "regression to the mean" is now widely known in the study of the effectiveness of remedial treatment of traffic accident blackspots. What happens is that the criterion used for selection of sites at which treatment is to be applied gives rise to bias in the estimate of the effectiveness: the conditional expectation of the after frequency is less than the true mean, even if the treatment is totally ineffective. It has been reported in some previous studies that accident "migration" has been observed. This is the phenomenon whereby the accident rate apparently rises at sites that are untreated but that are neighbours to treated sites. If this were a genuine effect, it would have serious implications for the assessment of remedial treatments. This paper aims to explain this migration effect in purely probabilistic terms, without recourse to the concept of physical migration. The model used is a new bivariate negative binomial distribution, incorporating spatial correlation between the true mean site accident rates. As with the regression to mean effect, the migration effect can then be explained in terms of the conditioning implicit in the selection process.

Accidents, Traffic↗
PubMed2004

A probabilistic and interactive decision-analysis system for unruptured intracranial aneurysms.

OBJECT: The goal in this study was to develop an interactive, probabilistic decision-analysis system for clinical use in the decision to treat or observe unruptured intracranial aneurysms. Further goals were to enable users of the system to adapt decision-analysis methods to individual patients and to provide a tool for interactive sensitivity analysis. METHODS: A computer program was designed to model the outcomes of treatment and observation of unruptured aneurysms. The user supplies probabilistic estimates of key parameters relating to a specific patient and nominates discount rate and quality of life adjustments. The program uses Monte Carlo discrete-event simulation methods to derive probability estimates of the outcomes of treatment and observation. Results are expressed as summary statistics and graphs. Discounted quality-adjusted life years are graphed using survival methods. Hierarchical simulations are used to enable investigators to perform probabilistic sensitivity analysis for one or multiple parameters simultaneously. The results of sensitivity analysis are expressed in graphs and as the expected value of perfect information. The system can be distributed and updated using the Internet. CONCLUSIONS: Further research is required into the benefits of clinical application of this system. Further research is also required into the optimum level of complexity of the model, into the user interface, and into how clinicians and patients are likely to interpret results. The system is easily adaptable to a range of medical decision analyses.

Confidence Intervals↗
PubMed2001

The Role of Inferences in Sequential Bargaining with One-Sided Incomplete Information: Some Experimental Evidence.

Two experiments tested a sequential bargaining model with one-sided incomplete information and time discounting. The findings suggest that although the comparative statics of the normative model are somewhat descriptive of the qualitative features of the data, bargainers do not conform to the signaling process that underlies bargaining models with incomplete information. Rather than assess and refine a probabilistic assessment of the private information based on the informed bargainer's behavior, uninformed bargainers infer their opponents' competitiveness. Further, bargainers are unable to use cost of delay in the strategic manner dictated by the equilibrium solution. The evidence suggests that although bargaining behavior is primarily determined by situational constraints, bargainers attribute their opponents' behavior to personal disposition, such as their level of competitiveness. Copyright 2001 Academic Press.

Journal Article↗
PubMed1997

Predicting the toxicity of complex mixtures using artificial neural networks.

Industrial and municipal wastewaters constitute major sources of contamination of the aquatic compartment and represent a threat to aquatic life. Artificial neural networks based on three different learning paradigms were studied as a means of predicting acute toxicity to trout (5 days exposure to wastewaters) using input data from two simple microbiotests requiring only 5 or 15 min of incubation. These microbiotests were 1) the chemoluminescent peroxidase (Cl-Per) assay, which can detect radical scavengers and enzyme-inhibiting substances, and 2) the luminescent bacteria toxicity test (Microtox), in which reduction of light emission by bacteria during exposure is taken as a measure of toxicity. The responses obtained with the trout bioassay, the Cl-Per and the Microtox test were analyzed through statistical correlation (Pearson product-moment correlation), unsupervised learning by a self-organizing network, and assisted learning by the backpropagation and the Boltzmann machine (probabilistic) paradigms. No significant correlation (p < 0.05) was found between the responses obtained with either the Cl-Per assay (p = 0.121) or the Microtox (p = 0.061) microbiotest and those resulting from the trout bioassay. The self-organizing network was able to identify by itself a maximum of five classes that were more or less relevant for predicting toxicity to fish: class 1 contained 2 samples that were toxic to fish, class 2 contained 2/3 samples that were toxic, class 3 showed 6/8 samples that were non toxic, class 4 contained 5/6 samples that were non-toxic and class 5 comprised one sample that was toxic. Supervised learning with backpropagation analysis yielded two kinds of networks that hold potential. The first one was able to predict the actual toxic wastewater concentration with an overall performance of 65% when fed fresh data, while the second one, which was designed to differentiate between toxic and non-toxic effluents, exhibited a much better performance (90%). However, the probabilistic network also proved to be a very good predictive model for toxicity to fish, with an overall performance of 90%. Although more data are needed, the network based on the backpropagation paradigm seems to be a better predictor or classifier of trout toxicity when used with the Cl-Per and the Microtox microbiotests.

Algorithms↗
PubMed2005

"Spooky actions at a distance": physics, psi, and distant healing.

Over decades, consciousness research has accumulated evidence of the real and measureable existence of "spooky actions at a distance"--modes of telepathy, telekinesis, clairvoyance, and the like. More recently scientists have begun rigorous study of the effects of distant healing intention and prayer vis-a-vis nonhuman living systems and patients in clinical trials. A barrier to taking such work seriously may be the belief that it is fundamentally incompatible with the scientific world view. This article suggests that it need not be; contemporary physics has generated a series of paradigms that can be used to make sense of, interpret, and explore "psi" and distant healing. Four such models are discussed, two drawn from relativity theory and two from quantum mechanics. First is the energetic transmission model, presuming the effects of conscious intention to be mediated by an as-yet unknown energy signal. Second is the model of path facilitation. As gravity, according to general relativity, "warps" space-time, easing certain pathways of movement, so may acts of consciousness have warping and facilitating effects on the fabric of the surrounding world. Third is the model of nonlocal entanglement drawn from quantum mechanics. Perhaps people, like particles, can become entangled so they behave as one system with instantaneous and unmediated correlations across a distance. Last discussed is a model involving actualization of potentials. The act of measurement in quantum mechanics collapses a probabilistic wave function into a single outcome. Perhaps conscious healing intention can act similarly, helping to actualize one of a series of possibilities; for example, recovery from a potentially lethal tumor. Such physics-based models are not presented as explanatory but rather as suggestive. Disjunctions as well as compatibilities between the phenomena of modern physics and those of psi and distant healing are explored.

Consciousness↗
PubMed1997

On integrating the techniques of direct methods with anomalous dispersion. IV. A simplified perturbation treatment for SAS phasing.

Results from probabilistic theory for the single-wavelength anomalous-scattering (SAS) Friedel pair, two-phase structure invariants, psi H = phi H + phi-H, are used to show that the SAS three-phase structure invariants, psi HK = phi H + phi K + phi-H-K, tend to positive values that are easily estimated. Appropriate averages of the estimates provide SAS perturbation corrections in the form of positive origin shifts for the probability distribution of psi HK values and for the tangent formula. The theoretical probabilistic results are verified by empirical statistical analyses of model-calculated phases and experimentally measured structure-factor magnitudes for a small-molecule and a protein crystal structure.

Chemistry, Physical↗
PubMed2006

Offline grammar-based recognition of handwritten sentences.

This paper proposes a sequential coupling of a Hidden Markov Model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using Stochastic Context-Free Grammars (SCFG) extracted from a text corpus. Based on extensive experiments, we conclude that syntax analysis helps to improve recognition rates significantly.

Algorithms↗
PubMed1994

A distributional approach to characterizing low-dose cancer risk.

Since cancer risk at very low doses cannot be directly measured in humans or animals, mathematical extrapolation models and scientific judgment are required. This article demonstrates a probabilistic approach to carcinogen risk assessment that employs probability trees, subjective probabilities, and standard bootstrapping procedures. The probabilistic approach is applied to the carcinogenic risk of formaldehyde in environmental and occupational settings. Sensitivity analyses illustrate conditional estimates of risk for each path in the probability tree. Fundamental mechanistic uncertainties are characterized. A strength of the analysis is the explicit treatment of alternative beliefs about pharmacokinetics and pharmacodynamics. The resulting probability distributions on cancer risk are compared with the point estimates reported by federal agencies. Limitations of the approach are discussed as well as future research directions.

Animals↗
PubMed2005

On construction of stochastic genetic networks based on gene expression sequences.

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

Animals↗
PubMed2001

Noncoding RNA gene detection using comparative sequence analysis.

BACKGROUND: Noncoding RNA genes produce transcripts that exert their function without ever producing proteins. Noncoding RNA gene sequences do not have strong statistical signals, unlike protein coding genes. A reliable general purpose computational genefinder for noncoding RNA genes has been elusive. RESULTS: We describe a comparative sequence analysis algorithm for detecting novel structural RNA genes. The key idea is to test the pattern of substitutions observed in a pairwise alignment of two homologous sequences. A conserved coding region tends to show a pattern of synonymous substitutions, whereas a conserved structural RNA tends to show a pattern of compensatory mutations consistent with some base-paired secondary structure. We formalize this intuition using three probabilistic "pair-grammars": a pair stochastic context free grammar modeling alignments constrained by structural RNA evolution, a pair hidden Markov model modeling alignments constrained by coding sequence evolution, and a pair hidden Markov model modeling a null hypothesis of position-independent evolution. Given an input pairwise sequence alignment (e.g. from a BLASTN comparison of two related genomes) we classify the alignment into the coding, RNA, or null class according to the posterior probability of each class. CONCLUSIONS: We have implemented this approach as a program, QRNA, which we consider to be a prototype structural noncoding RNA genefinder. Tests suggest that this approach detects noncoding RNA genes with a fair degree of reliability.

Algorithms↗
PubMed2004

Mining for associations between text and brain activation in a functional neuroimaging database.

We describe a method for mining a neuroimaging database for associations between text and brain locations. The objective is to discover association rules between words indicative of cognitive function as described in abstracts of neuroscience papers and sets of reported stereotactic Talairach coordinates. We invoke a simple probabilistic framework in which kernel density estimates are used to model distributions of brain activation foci conditioned on words in a given abstract. The principal associations are found in the joint probability density between words and voxels. We show that the statistically motivated associations are well aligned with general neuroscientific knowledge.

Brain↗
PubMed2005

Genrate: a generative model that finds and scores new genes and exons in genomic microarray data.

Recently, researchers have made some progress in using microarrays to validate predicted exons in genome sequence and find new gene structures. However, current methods rely on separately making threshold-based decisions on intensity of expression, similarity of expression profiles, and arrangements of exons in the genome. We have taken a Bayesian approach and developed GenRate, a generative model that accounts for both genome-wide expression data taken from multiple conditions (e.g. tissues) and co-location and density of probes in DNA sequence data. GenRate balances probabilistic evidence derived from different sources and outputs scores (log-likelihoods) for each gene model, enabling the estimation of false-positive and false-negative rates. The model has a number of local minima that is exponential in the length of the DNA sequence data, so direct application of the EM learning algorithm produces poor results. We describe a novel way of parameterizing the model using examples from the data set, so that good solutions are found using an efficient algorithm. We apply GenRate to a subset of mouse genome-wide expression data that we have created, and discuss the statistical significance of the genes found by GenRate. Three of the highest-ranking gene structures found by GenRate, each containing thousands of bases from the genome, are confirmed using RT-PCR experiments.

Animals↗
PubMed1990

Methodology of ECG interpretation in the Hannover program.

The Hannover ECG program HES has been designed for measurement and interpretation of resting and (moderate) exercise electrocardiograms. In the signal analysis part the program follows an averaging strategy. For diagnostic classification a hybrid model with decision trees and scoring algorithms, and with multivariate probabilistic tests for derivation of category A statements is applied. The multivariate classification technique allows to adjust sensitivity and specificity for specific application areas without changing the diagnostic criteria.

Algorithms↗