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Results for “probabilistic modelling”

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At least 1,423 records · Page 79Linked to original sources
PubMed1987

Drug-receptor relationships, selection of therapeutic goals, and adaptive control of pharmacokinetic systems.

Drug-receptor relationships are governed by a host of environmental and physiological influences, which reduce the predictability of the therapeutic response to an administered dose of a drug. The management of patient therapy is aided by models that encompass the broadest aspects of the situation, from the probabilistic aspects of drug administration to the accuracy of the assay procedures for evaluating therapeutic efficacy.

Aminoglycosides↗
PubMed1998

On the use of Bayesian methods for evaluating compartmental neural models.

Computational modeling is being used increasingly in neuroscience. In deriving such models, inference issues such as model selection, model complexity, and model comparison must be addressed constantly. In this article we present briefly the Bayesian approach to inference. Under a simple set of commonsense axioms, there exists essentially a unique way of reasoning under uncertainty by assigning a degree of confidence to any hypothesis or model, given the available data and prior information. Such degrees of confidence must obey all the rules governing probabilities and can be updated accordingly as more data becomes available. While the Bayesian methodology can be applied to any type of model, as an example we outline its use for an important, and increasingly standard, class of models in computational neuroscience--compartmental models of single neurons. Inference issues are particularly relevant for these models: their parameter spaces are typically very large, neurophysiological and neuroanatomical data are still sparse, and probabilistic aspects are often ignored. As a tutorial, we demonstrate the Bayesian approach on a class of one-compartment models with varying numbers of conductances. We then apply Bayesian methods on a compartmental model of a real neuron to determine the optimal amount of noise to add to the model to give it a level of spike time variability comparable to that found in the real cell.

Action Potentials↗
PubMed2001

Causation in epidemiology.

Causation is an essential concept in epidemiology, yet there is no single, clearly articulated definition for the discipline. From a systematic review of the literature, five categories can be delineated: production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic. Strengths and weaknesses of these categories are examined in terms of proposed characteristics of a useful scientific definition of causation: it must be specific enough to distinguish causation from mere correlation, but not so narrow as to eliminate apparent causal phenomena from consideration. Two categories-production and counterfactual-are present in any definition of causation but are not themselves sufficient as definitions. The necessary and sufficient cause definition assumes that all causes are deterministic. The sufficient-component cause definition attempts to explain probabilistic phenomena via unknown component causes. Thus, on both of these views, heavy smoking can be cited as a cause of lung cancer only when the existence of unknown deterministic variables is assumed. The probabilistic definition, however, avoids these assumptions and appears to best fit the characteristics of a useful definition of causation. It is also concluded that the probabilistic definition is consistent with scientific and public health goals of epidemiology. In debates in the literature over these goals, proponents of epidemiology as pure science tend to favour a narrower deterministic notion of causation models while proponents of epidemiology as public health tend to favour a probabilistic view. The authors argue that a single definition of causation for the discipline should be and is consistent with both of these aims. It is concluded that a counterfactually-based probabilistic definition is more amenable to the quantitative tools of epidemiology, is consistent with both deterministic and probabilistic phenomena, and serves equally well for the acquisition and the application of scientific knowledge.

Causality↗
PubMed2004

Fluctuation of predictions of a connectionist model can lead to qualitatively different results.

Several methods have been proposed to derive ideal predictions of connectionist models. In contrast, there have been few attempts to explore fluctuations of the predictions around the ideal value in probabilistic environment. This void must be filled. Thus, Monte-Carlo simulations with the Rescorla-Wagner model were conducted for an experimental paradigm called relative validity. Simulations indicated that fluctuation is too sizeable to neglect and that it can even lead to qualitatively different predictions. The final associative strength was influenced by the last several trials.

Cues↗
PubMed2005

Advances in classification of EEG signals via evolving fuzzy classifiers and dependant multiple HMMs.

Two novel approaches to the problem of brain signals (electroencephalogram (EEG)) classification are introduced in the paper. The first method is based on a modular probabilistic network architecture that employs multiple dependant hidden Markov models (DM-HMM-D) on the input features (channels). The second method, eClass, is based on an on-line evolvable fuzzy rule base of EEG signal prototypes that represent each class and take into consideration the spatial proximity between input signals. Both approaches use supervised learning but differ in their mode of operation. eClass is designed recursively, on-line, and has an evolvable structure, while DM-HMM-D is trained off-line, in a block-based mode, and has a fixed architecture. Both methods have been extensively tested on real EEG data that is recorded during several experimental sessions involving a single female subject who is exposed to mild pain induced by a laser beam. Experimental results illustrate the viability of the proposed approaches and their potential in solving similar classification problems.

Adult↗
PubMedDate not supplied

Evaluation of safety of hypobaric decompressions and EVA from positions of probabilistic theory.

Formation and subsequent evolution of gas bubbles in blood and tissues of subjects exposed to decompression are casual processes in their nature. Such character of bubbling processes in a body predetermines probabilistic character of decompression sickness (DCS) incidence in divers, aviators and astronauts. Our original probabilistic theory of decompression safety is based on stochastic models of these processes and on the concept of critical volume of a free gas phase in body tissues. From positions of this theory, the probability of DCS incidence during single-stage decompressions and during hypobaric decompressions under EVA in particular, is defined by the distribution of possible values of nucleation efficiency in "pain" tissues and by its critical significance depended on the parameters of a concrete decompression. In the present study the following is shown: 1) the dimensionless index of critical nucleation efficiency for "pain" body tissues is a more adequate index of decompression stress in comparison with Tissue Ratio, TR; 2) a priory the decompression under EVA performed according to the Russian protocol is more safe than decompression under EVA performed in accordance with the U.S. protocol; 3) the Russian space suit operated at a higher pressure and having a higher "rigidity" induces a stronger inhibition of mechanisms of cavitation and gas bubbles formation in tissues of a subject located in it, and by that provides a more considerable reduction of the DCS risk during real EVA performance.

Aerospace Medicine↗
PubMed2003

A statistical explanation of visual space.

The subjective visual space perceived by humans does not reflect a simple transformation of objective physical space; rather, perceived space has an idiosyncratic relationship with the real world. To date, there is no consensus about either the genesis of perceived visual space or the implications of its peculiar characteristics for visually guided behavior. Here we used laser range scanning to measure the actual distances from the image plane of all unoccluded points in a series of natural scenes. We then asked whether the differences between real and apparent distances could be explained by the statistical relationship of scene geometry and the observer. We were able to predict perceived distances in a variety of circumstances from the probability distribution of physical distances. This finding lends support to the idea that the characteristics of human visual space are determined probabilistically.

Humans↗
PubMed2004

Likert and Guttman scaling of visual function rating scale questionnaires.

PURPOSE: To test the assumptions underlying Likert scoring of visual function questionnaires. METHODS: Questionnaires were administered to 284 low-vision subjects by telephone. Each subject was administered two of four questionnaires: ADVS, NEI VFQ-25 plus supplement, expanded VAQ, and VF-14. RESULTS: Z-scores for cumulative frequency of using each rating category across subjects are not linear with rating category rank and items are not the same difficulty for any of the questionnaires. Guttmann coefficients of reproducibility ranged from 57% for the ADVS to 51% for the NEI VFQ-25. Cronbach alphas ranged from 0.92 for the VF-14 to 0.96 for the NEI VFQ; however, inter-item consistency coefficients ranged from 0.24 for the VAQ to 0.45 for the NEI VFQ. Likert scores were significantly correlated between instruments, ranging from 0.66 for NEI VFQ vs ADVS to 0.90 for the VF-14 vs. ADVS. CONCLUSIONS: The rating scales of all four questionnaires fail to satisfy Likert's assumptions. Also, ratings are probabilistic, rather than deterministic, which means that the Likert model is not valid for these questionnaires. However, Likert scores for all four instruments are intercorrelated, suggesting that they are monotonic with the latent subject trait distributed in the low vision sample.

Adult↗
PubMed1970

The reliability of neurons.

The prevalent probabilistic view is virtually untestable; it remains a plausible belief. The cases usually cited can not be taken as evidence for it. Several grounds for this conclusion are developed. Three issues are distinguished in an attempt to clarify a murky debate: (a) the utility of probabilistic methods in data reduction, (b) the value of models that assume indeterminacy, and (c) the validity of the inference that the nervous system is largely indeterministic at the neuronal level. No exception is taken to the first two; the second is a private heuristic question. The third is the issue to which the assertion in the first two sentences is addressed. Of the two kinds of uncertainty, statistical mechanical (= practical unpredictability) as in a gas, and Heisenbergian indeterminancy, the first certainly exists, the second is moot at the neuronal level. It would contribute to discussion to recognize that neurons perform with a degree of reliability. Although unreliability is difficult to establish, to say nothing of measure, evidence that some neurons have a high degree of reliability, in both connections and activity is increasing greatly. An example is given from sternarchine electric fish.

Animals↗
PubMed2005

Regression-based association analysis with clustered haplotypes through use of genotypes.

Haplotype-based association analysis has been recognized as a tool with high resolution and potentially great power for identifying modest etiological effects of genes. However, in practice, its efficacy has not been as successfully reproduced as expected in theory. One primary cause is that such analysis tends to require a large number of parameters to capture the abundant haplotype varieties, and many of those are expended on rare haplotypes for which studies would have insufficient power to detect association even if it existed. To concentrate statistical power on more-relevant inferences, in this study, we developed a regression-based approach using clustered haplotypes to assess haplotype-phenotype association. Specifically, we generalized the probabilistic clustering methods of Tzeng to the generalized linear model (GLM) framework established by Schaid et al. The proposed method uses unphased genotypes and incorporates both phase uncertainty and clustering uncertainty. Its GLM framework allows adjustment of covariates and can model qualitative and quantitative traits. It can also evaluate the overall haplotype association or the individual haplotype effects. We applied the proposed approach to study the association between hypertriglyceridemia and the apolipoprotein A5 gene. Through simulation studies, we assessed the performance of the proposed approach and demonstrate its validity and power in testing for haplotype-trait association.

Apolipoprotein A-V↗
PubMed2003

Methodology for evaluation of possible consequences of accidental atmospheric releases of hazardous matter.

Sites exist with high levels of risk of accidental atmospheric releases. These releases can be hazardous nuclear, chemical, and biological matter. Such accidents may occur during transport of waste, or they may be due to natural hazards, human errors, terror acts or various operations at high risk. Considering the operation of lifting and transport of the sunken Kursk nuclear submarine as an example, a methodology for risk assessment is described. This methodology includes two approaches: (1) probabilistic analysis of possible atmospheric transport pathways using trajectory modelling, and (2) evaluation of possible contamination and consequences using real-time operational atmospheric dispersion modelling. The first approach can be applied in advance of an operation during the preparation stage, the second in real time during the operation stage. For the cases considered in this study, the results of trajectory modelling are supported by the operational dispersion modelling, i.e., the westerly flow is dominant during fall occurring 79% of the time. Hence, September-October 2001 was more appropriate for the lifting and transport of the Kursk nuclear submarine in comparison with summer months, when atmospheric transport toward the populated regions of the Kola and Scandinavian Peninsulas was dominant. The suggested methodology may be applied to any potentially dangerous object involving a risk of atmospheric release of hazardous material of nuclear, chemical or biological nature.

Accidents↗
PubMed2005

Integrating seasonal climate prediction and agricultural models for insights into agricultural practice.

Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate-crop-economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based 'discussion support systems' contribute to learning and farmer-researcher dialogue. Integrated climate-crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis.

Climate↗
PubMed2005

Predicting failure using conditioning on damage history: demonstration on percolation and hierarchical fiber bundles.

We formulate the problem of probabilistic predictions of global failure in the simplest possible model based on site percolation and on one of the simplest models of time-dependent rupture, a hierarchical fiber bundle model. We show that conditioning the predictions on the knowledge of the current degree of damage (occupancy density p or number and size of cracks) and on some information on the largest cluster improves significantly the prediction accuracy, in particular by allowing one to identify those realizations which have anomalously low or large clusters (cracks). We quantify the prediction gains using two measures, the relative specific information gain (which is the variation of entropy obtained by adding new information) and the root mean square of the prediction errors over a large ensemble of realizations. The bulk of our simulations have been obtained with the two-dimensional site percolation model on a lattice of size L x L=20 x 20 and hold true for other lattice sizes. For the hierarchical fiber bundle model, conditioning the measures of damage on the information of the location and size of the largest crack extends significantly the critical region and the prediction skills. These examples illustrate how ongoing damage can be used as a revelation of both the realization-dependent preexisting heterogeneity and the damage scenario undertaken by each specific sample.

Journal Article↗
PubMed2004

Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers.

BACKGROUND: Controlling costs and achieving health care quality improvements require the participation of activated and informed consumers and patients. OBJECTIVES: We describe a process for conceptualizing and operationalizing what it means to be "activated" and delineate the process we used to develop a measure for assessing "activation," and the psychometric properties of that measure. METHODS: We used the convergence of the findings from a national expert consensus panel and patient focus groups to define the concept and identify the domains of activation. These domains were operationalized by constructing a large item pool. Items were pilot-tested and initial psychometric analysis performed using Rasch methodology. The third stage refined and extended the measure. The fourth stage used a national probability sample to assess the measure's psychometric performance overall and within different subpopulations. STUDY SAMPLE: Convenience samples of patients with and without chronic illness, and a national probability sample (N=1,515) are included at different stages in the research. CONCLUSIONS: The Patient Activation Measure is a valid, highly reliable, unidimensional, probabilistic Guttman-like scale that reflects a developmental model of activation. Activation appears to involve four stages: (1) believing the patient role is important, (2) having the confidence and knowledge necessary to take action, (3) actually taking action to maintain and improve one's health, and (4) staying the course even under stress. The measure has good psychometric properties indicating that it can be used at the individual patient level to tailor intervention and assess changes.

Adult↗
PubMed2002

A comparative study on kernel-based probabilistic neural networks for speaker verification.

This paper compares kernel-based probabilistic neural networks for speaker verification based on 138 speakers of the YOHO corpus. Experimental evaluations using probabilistic decision-based neural networks (PDBNNs), Gaussian mixture models (GMMs) and elliptical basis function networks (EBFNs) as speaker models were conducted. The original training algorithm of PDBNNs was also modified to make PDBNNs appropriate for speaker verification. Results show that the equal error rate obtained by PDBNNs and GMMs is less than that of EBFNs (0.33% vs. 0.48%), suggesting that GMM- and PDBNN-based speaker models outperform the EBFN ones. This work also finds that the globally supervised learning of PDBNNs is able to find decision thresholds that not only maintain the false acceptance rates to a low level but also reduce their variation, whereas the ad-hoc threshold-determination approach used by the EBFNs and GMMs causes a large variation in the error rates. This property makes the performance of PDBNN-based systems more predictable.

Algorithms↗
PubMed2004

Nonlinear system modelling via optimal design of neural trees.

This paper introduces a flexible neural tree model. The model is computed as a flexible multi-layer feed-forward neural network. A hybrid learning/evolutionary approach to automatically optimize the neural tree model is also proposed. The approach includes a modified probabilistic incremental program evolution algorithm (MPIPE) to evolve and determine a optimal structure of the neural tree and a parameter learning algorithm to optimize the free parameters embedded in the neural tree. The performance and effectiveness of the proposed method are evaluated using function approximation, time series prediction and system identification problems and compared with the related methods.

Algorithms↗
PubMed2003

Data selection and natural sampling: probabilities do matter.

Probabilistic accounts of Wason's selection task (Oaksford & Chater, 1994, 1996) are controversial, with some researchers failing to replicate the predicted effects of probability manipulations. This paper reports a single experiment in which participants sampled the data naturally-that is, sequentially. The proportions of possible data types (i.e., cards in the selection task) also reflected the probability manipulation. Other than this procedural difference, the materials were the same as those in Oberauer, Wilhelm, and Rosas-Diaz's (1999) Experiment 3, which failed to show probabilistic effects. Significant probabilistic effects were observed. Moreover, in a comparative model-fitting exercise, a revised version of the information gain model (Hattori, 1999, 2002; Oaksford & Chater, in press-b) was shown to provide better fits to these data than did competing explanations.

Choice Behavior↗
PubMed2005

On the slow convergence of EM and VBEM in low-noise linear models.

We analyze convergence of the expectation maximization (EM) and variational Bayes EM (VBEM) schemes for parameter estimation in noisy linear models. The analysis shows that both schemes are inefficient in the low-noise limit. The linear model with additive noise includes as special cases independent component analysis, probabilistic principal component analysis, factor analysis, and Kalman filtering. Hence, the results are relevant for many practical applications.

Algorithms↗