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At least 919 records · Page 51Linked to original sources
PubMed2005

A new explanatory model of an SIR disease epidemic: a knowledge-based, probabilistic approach to epidemic analysis.

A new explanatory model for epidemic analysis is presented; it has a knowledge based component and a probabilistic computational component. The former assembles details of household characteristics, social networks and connectivity in the community--'knowledge'--which is used to determine the structure of the computational component. The latter links individuals and households through statistically-defined opportunities for contacts and, by repeated trials, determines an average longitudinal time course (epidemic curve) of the simulated infection as it spreads through the community from inception to extinction of the epidemic. The model thus aims to describe the epidemic itself, rather than any abstraction of it. In application to a 1955-56, self-contained epidemic of an SIR disease, variola minor, the model generates 1 dominant longitudinal pattern that matches closely the epidemic curve of observed daily case rates; it is suggested that other patterns indicate different ways in which the epidemic might have evolved. The model can be used to show how differing community characteristics would affect the simulated epidemic.

Brazil↗
PubMed2006

Probabilistic evaluation of ammonia toxicity in Milwaukee's Outer Harbor.

Water-quality models that are simple yet sound and reliable and that correspond to water-quality criteria that include magnitude, frequency, and duration components are needed. Monte Carlo models are developed on the basis of available flow and water-quality data and a deterministic water-quality model appropriate for the problem at hand and the data available. Monte Carlo models yield time series and probability distributions of constituents of interest in conformance with water-quality criteria. The application of a Monte Carlo model to the probabilistic evaluation of ammonia toxicity in Milwaukee's Outer Harbor is presented here. Under typical operating conditions for the Jones Island Wastewater Treatment Plant, ammonia toxicity was found to not be a problem for the Outer Harbor. The Monte Carlo model then was used to determine effluent limits that would meet the ammonia toxicity criteria.

Ammonia↗
PubMed1997

Potential effects of climatic change on radiological doses from disposal of Canadian nuclear fuel waste.

The environmental assessment of deep geologic disposal of Canadian nuclear fuel waste considers many processes that could affect radionuclide transport to humans over thousands of years. Climatic change is an important feature that will occur over these long times. Glaciation will likely occur within the next 100,000 years over much of Canada, and its impact on radiological doses has been assessed previously. In the present study, we investigate the potential effect of short-term climatic change, usually associated with global warming caused by increases in atmospheric trace gases. We study the main biosphere transport pathways causing a radiological dose to humans from 129I, which is the most important radionuclide in disposal of Canadian used nuclear fuel. Irrigation of a garden with contaminated well water is the main pathway and it can be affected by changes in temperature and precipitation. A cold, wet climate decreases the need for irrigation, and this decreases the radiological dose. A drastic climatic change, such as an increase in temperature from 10 to 20 degrees C and decrease in precipitation from 0.3 to 0.2 m during the growing season, is estimated to increase the dose by a factor of four. This is a relatively small change compared to the range of doses that arise from the variability and uncertainty in many of the parameters used in the environmental assessment models. Therefore, it is likely that the results of probabilistic dose assessment models can include the consequences of short-term climatic change.

Agriculture↗
PubMed2001

Perturbative approach to the Bak-Sneppen model.

We study the Bak-Sneppen model in the probabilistic framework of the run time statistics (RTS). This model has attracted a large interest for its simplicity being a prototype for the whole class of models showing self-organized criticality. The dynamics is characterized by a self-organization of almost all the species fitnesses above a nontrivial threshold value, and by a lack of spatial and temporal characteristic scales. This results in avalanches of activity power law distributed. In this Letter we use the RTS approach to compute the value of x(c), the value of the avalanche exponent tau, and the asymptotic distribution of minimal fitnesses.

Journal Article↗
PubMed1987

A neural net model for the release of prolactin during pseudopregnancy in the rat.

A theoretical probabilistic neural net model is proposed here based on the interaction between two subsystems, the connections of which are made up by means of chemical markers. The activity of such a system at t = n tau is exclusively dependent on the firing record of the system at t = (n-1) tau, on the level of spontaneous activity and on an external inhibitory influence which we have found that may play an important role in the prolactin release level during pseudopregnancy in rats. We explore some of the implications of this model on the possible role of the hippocampus in the phenomenon.

Animals↗
PubMedDate not supplied

Probabilistic analysis of cost-effectiveness models: choosing between treatment strategies for gastroesophageal reflux disease.

When choosing between mutually exclusive treatment options, it is common to construct a cost-effectiveness frontier on the cost-effectiveness plane that represents efficient points from among the treatment choices. Treatment options internal to the frontier are considered inefficient and are excluded either by strict dominance or by appealing to the principle of extended dominance. However, when uncertainty is considered, options excluded under the baseline analysis may form part of the cost-effectiveness frontier. By adopting a Bayesian approach, where distributions for model parameters are specified, uncertainty in the decision concerning which treatment option should be implemented is addressed directly. The approach is illustrated using an example from a recently published cost-effectiveness analysis of different possible treatment strategies for gastroesophageal reflux disease. It is argued that probabilistic analyses should be encouraged because they have potential to quantify the strength of evidence in favor of particular treatment choices.

Bayes Theorem↗
PubMed2004

On the modeling, construction, and evaluation of a probabilistic atlas of brain perfusion.

To detect subtle, abnormal perfusion patterns in brain single photon emission computer tomography (SPECT) images, it is necessary to develop quantitative methods in which computer-aided statistical analysis takes advantage of information present in databases of normal subjects. The purpose of this study was to evaluate and examine aspects of the creation and the modeling power of three statistical models for representing brain perfusion as observed in ECD-SPECT. The first model is a local model of voxel-by-voxel mean and variance. The second model is a PCA-based global model that accounts for covariance patterns in the images. The third model is an original model that is a non-linear extension to the second model. This model is based on robust statistics for modeling abnormalities. To evaluate the models, a leave-one-out procedure combined with simulations of abnormal perfusion patterns was adopted. Abnormal perfusion patterns were simulated at different locations in the brain, with different intensities and different sizes. The procedure yields receiver operator characteristics (ROC) that present a combined measure of model-fit and model-sensitivity at detecting abnormalities. The scheme can further be used to compare models as well as the influence of different preprocessing steps. In particular, the influence of different registration approaches is studied and analyzed. The results show that the original non-linear model always performed better than the other models. Finally, location-dependent detection performance was found. Most notably, a higher variation of perfusion was observed in the right frontal cortex than in the other locations studied.

Algorithms↗
PubMed2005

Coarse-grained model of the interaction of light with polymeric material: onset of ablation.

A coarse-grained model has been developed for molecular dynamics simulations of the interaction of light with polymeric materials. The photon energy can result in a vibrational excitation (photothermal process) or disruption of a chemical bond (photochemical process) in a polymer. In the latter case, the formation of active radial sites and the occurrence of chemical reactions have to be taken into consideration. The novel feature of this model is the incorporation of chemical reactions into the united atom approximate representation of the polymer structure, which permits the study of laser ablation, degradation, or the effect of various chemical reactions on large time and length scales. The chemical reactions are included in the model in a probabilistic manner as in the kinetic Monte Carlo method. This model adopts physically and experimentally known quantities such as enthalpies and probabilities of reactions. Properties such as laser irradiation time, laser fluence, and wavelength are explicitly included. Moreover, no chemically correct interaction potential is required to incorporate the effects of chemical reactions on the dynamics of the system after energy deposition. We find that the model provides a plausible description of the essential processes. The laser-induced pressure relaxation is the main mechanism responsible for the onset of polymer ablation. Since the pressure relaxation processes are slow, there is a delay in the onset of ablation after the end of the laser pulse as is observed experimentally. The vaporization processes are not efficient for material removal, and the effect is minimal for both photochemical and photothermal processes. A lower fluence is needed for the onset of ablation with photochemical processes than photothermal processes.

Light↗
PubMed1995

Modelling on rural out-migration system: a probabilistic approach.

"The aim of this paper is to develop a probability model describing the variation in...total out-migration [using] micro-level data. The parameters involved in the models are estimated with the help of [the] method of moment, proportion of zeroth cell and one'th cell respectively, [and] the suitability of [the] model [is] tested through observed demographic survey data [for India]."

Asia↗
PubMed2005

Is inferential reasoning just probabilistic reasoning in disguise?

Oaksford, Chater, and Larkin (2000) have suggested that people actually use everyday probabilistic reasoning when making deductive inferences. In two studies, we explicitly compared probabilistic and deductive reasoning with identical if-then conditional premises with concrete content. In the first, adults were given causal premises with one strongly associated antecedent and were asked to make standard deductive inferences or to judge the probabilities of conclusions. In the second, reasoners were given scenarios presenting a causal relation with zero to three potential alternative antecedents. The participants responded to each set of problems under both deductive and probabilistic instructions. The results show that deductive and probabilistic inferences are not isomorphic. Probabilistic inferences can model deductive responses only using a limited, very high threshold model, which is equivalent to a simple retrieval model. These results provide a clearer understanding of the relations between probabilistic and deductive inferences and the limitations of trying to consider these two forms of inference as having a single underlying process.

Humans↗
PubMed2002

Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks.

MOTIVATION: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. RESULTS: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.

Cell Cycle↗
PubMed2006

Probabilistic settling in the Local Exchange Model of turbulent particle transport.

The Local Exchange Model (LEM) is a stochastic diffusion model of particle transport in turbulent flowing water. It was developed mainly for application to particles of near-neutral buoyancy that are strongly influenced by turbulent eddies. Turbulence can rapidly transfer such particles to the bed, where settlement can then occur by, for example, sticking to biofilms (e.g., fine particulate organic matter, or FPOM) or attaching to the substrate behaviorally (e.g., benthic invertebrates). Previous papers on the LEM have addressed the problems of how long (time) and far (distance) a suspended particle will be transported before hitting the bed for the first time. These are the hitting-time and hitting-distance problems, respectively. Hitting distances predicted by the LEM for FPOM in natural streams tend to be much shorter than the distances at which most particles actually settle, suggesting that particles usually do not settle the first time they hit the bed. The present paper extends the LEM so it can address probabilistic settling, where a particle encountering the bed can either remain there for a positive length of time (i.e., settle) or immediately reflect back into the water column, each with positive probability. Previous results for the LEM are generalized by deducing a single set of equations governing the probability distribution and moments of a broad class of quantities that accumulate during particle trajectories terminated by hitting or settling on the bed (e.g., transport time, transport distance, cumulative energy expenditure during transport). Key properties of the settling-time and settling-distance distributions are studied numerically and compared with the observed FPOM settling-distance distribution for a natural stream. Some remaining limitations of the LEM and possible means of overcoming them are discussed.

Geologic Sediments↗
PubMed1984

Detection and identification of spatial frequency: models and data.

Detection and identification of up to four simple sinusoidal gratings were studied. The experimental results were quantitatively compared to predictions from several models. The models all assumed probabilistically independent channels sensitive to different ranges of spatial frequency. The models differed in the shapes of their underlying distributions and, for detection, their decision rule. Detection and identification of far-apart spatial frequencies were consistent with these models. Thus, uncertainty effects for both detection and identification (the decrease in performance with an increase in the number of possible spatial frequencies) can be explained without assuming that attention capacity is limited.

Attention↗
PubMed2005

Parsing with probabilistic strictly locally testable tree languages.

Probabilistic k-testable models (usually known as k-gram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events during pattern classification. In this paper, we introduce the family of stochastic k-testable tree languages and describe how these models can approximate any stochastic rational tree language. The model is applied to the task of learning a probabilistic k-testable model from a sample of parsed sentences. In particular, a parser for a natural language grammar that incorporates smoothing is shown.

Algorithms↗
PubMed2001

Modeling acute toxicity of chemicals to Daphnia magna: a probabilistic neural network approach.

A methodology based on probabilistic neural networks (PNNs) is applied to model the acute toxicity (48-h LC50) of a set of 700 highly diverse chemicals to Daphnia magna. First, cross-validation experiments confirming the potential use of the PNN as modeling tool for the problem at hand were performed. Next, various approaches to construct-improved models are presented. The resulting four models are then validated using an external test set of 76 additional compounds. Input to the PNNs is derived solely from simple molecular descriptors and structural fragments and excludes bulk property parameters, such as the water solubility or the octanol/water partition coefficient.

Animals↗
PubMed2006

Continuous probabilistic approach to species dynamics in Hubbell's zero-sum local community.

In this paper a continuous probabilistic approach formulated using Kolmogorov-Fokker-Planck forward and backward models is applied to Hubbell's zero-sum neutral theory for species dynamics in local community. Using this technique the probability density of species abundance, distribution of the first passage time to extinction or fixation and probability of extinction are defined. The resulting values for the distribution of the first passage time to extinction are verified by the simulation study of Hubbell's zero-sum neutral model for the local community. Based on the sensitivity analysis for the continuous probabilistic models, the realistic classification of local communities subject to their diversity and species dynamics is proposed with respect to the immigration probability, the species metacommunity relative abundance, and the size of local community.

Computer Simulation↗
PubMed1995

Calibration of a bubble evolution model to observed bubble incidence in divers.

The method of maximum likelihood was used to calibrate a probabilistic bubble evolution model against data of bubbles detected in divers. These data were obtained from a diverse set of 2,064 chamber man-dives involving air and heliox with and without oxygen decompression. Bubbles were measured with Doppler ultrasound and graded according to the Kisman-Masurel code from which a single maximum bubble grade (BG) per diver was compared to the maximum bubble radius (Rmax) predicted by the model. This comparison was accomplished using multinomial statistics by relating BG to Rmax through a series of probability functions. The model predicted the formation of the bubble according to the critical radius concept and its evolution was predicted by assuming a linear rate of inert gas exchange across the bubble boundary. Gas exchange between the model compartment and blood was assumed to be perfusion-limited. The most successful calibration of the model was found using a trinomial grouping of BG according to no bubbles, low, and high bubble activity, and by assuming a single tissue compartment. Parameter estimations converge to a tissue volume of 0.00036 cm3, a surface tension of 5.0 dyne.cm-1, respective time constants of 27.9 and 9.3 min for nitrogen and helium, and respective Ostwald tissue solubilities of 0.0438 and 0.0096. Although not part of the calibration algorithm, the predicted evolution of bubble size compares reasonably well with the temporal recordings of BGs.

Diffusion↗