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At least 415 records · Page 23Linked to original sources
PubMed1996

Modelling ECG signals with hidden Markov models.

In this paper, we have studied the use of continuous probability density function hidden Markov models for the ECG signal analysis problem. Our previous work has focused on syntactic pattern recognition methods in signal processing. Hidden Markov model is basically a non-deterministic probabilistic finite state machine, which can be constructed inductively. It has been widely used in speech recognition and DNA modelling. We have found that hidden Markov models are very suitable for ECG recognition and analysis problems and that they are able to model accurately segmented ECG signals.

Artificial Intelligence↗
PubMed2003

Posterior probability maps and SPMs.

This technical note describes the construction of posterior probability maps that enable conditional or Bayesian inferences about regionally specific effects in neuroimaging. Posterior probability maps are images of the probability or confidence that an activation exceeds some specified threshold, given the data. Posterior probability maps (PPMs) represent a complementary alternative to statistical parametric maps (SPMs) that are used to make classical inferences. However, a key problem in Bayesian inference is the specification of appropriate priors. This problem can be finessed using empirical Bayes in which prior variances are estimated from the data, under some simple assumptions about their form. Empirical Bayes requires a hierarchical observation model, in which higher levels can be regarded as providing prior constraints on lower levels. In neuroimaging, observations of the same effect over voxels provide a natural, two-level hierarchy that enables an empirical Bayesian approach. In this note we present a brief motivation and the operational details of a simple empirical Bayesian method for computing posterior probability maps. We then compare Bayesian and classical inference through the equivalent PPMs and SPMs testing for the same effect in the same data.

Algorithms↗
PubMed2003

Bayesian second-level analysis of functional magnetic resonance images.

We propose a new method for the second-level analysis of functional MRI data based on Bayesian statistics. Our method does not require a computationally costly Bayesian model on the first level of analysis. Rather, modeling for single subjects is realized by means of the commonly applied General Linear Model. On the basis of the resulting parameter estimates for single subjects we calculate posterior probability maps and maps of the effect size for effects of interest in groups of subjects. A comparison of this method with the conventional analysis based on t statistics shows that the new approach is more robust against outliers. Moreover, our method overcomes some of the severe problems of null hypothesis significance tests such as the need to correct for multiple comparisons and facilitates inferences which are hard to formulate in terms of classical inferences.

Algorithms↗
PubMed1997

Constructing probabilistic models.

Bayesian networks have become one of the most popular probabilistic techniques in AI, largely due to the development of several efficient inference algorithms. In this paper we describe a heuristic method for constructing Bayesian networks. Our construction method relies on the relationship between Bayesian networks and decomposable models, a special kind of graphical model. We explain this relationship and then show how it can be used to facilitate model construction. Finally, we describe an implemented computer program that illustrates these ideas.

Algorithms↗
PubMed1997

Information theoretical approach to constitution and reduction of medical data.

In medical decision problems it is very important to use the most relevant piece of information for decision making. We focus on a special case of diagnostic decision making when we can measure many symptoms and signs and we have to make diagnostic conclusions. We can state the problem as follows. We can measure symptoms and signs of a patient, denoted by s1, s2, ..., sk, and we have to decide about a possible diagnosis d. We know that the symptoms and signs have different costs w1, w2, ... wk when they are examined. Of course, each symptom, sign or their combination has a different predictive value for the diagnosis. Our task is to find out the combination of symptoms from given data with a sufficient informative value for diagnostic decision making. However, simultaneously we look for a combination of symptoms and signs with minimal costs among those carrying sufficient information. For that reason we will describe approaches based on information measures of statistical dependence and to show the idea of the program CORE (constitution and reduction of data) prepared for practical applications in medicine.

Algorithms↗
PubMed1979

Probabilities of concordance of twins with respect to genetic markers. A general formulation.

The formulas needed in the determination of monozygosity in twin pairs using genetic markers are derived and presented. A general formula for the calculation of concordance probabilities independent of gene frequencies and allele number is derived, enabling either manual computation or computer programming for any Mendelian markers usable in twin zygosity diagnosis.

Female↗
PubMed1979

Dependency of concordance probability on gene frequencies in genetic systems for the diagnosis of twin zygosity. A graphical presentation enabling the rapid, optimal choice of genetic system.

The dependency of probabilities of phenotypic concordance of gene frequencies in three-allele genetic systems is presented. A graphical display enables the rapid comparison of the relative effectiveness of different systems, taking into account dominance relationships within each genetic system. Four or more allele systems can also be approximated, while two-allele systems are considered to be special cases of three-allele ones.

Alleles↗
PubMed1984

Systematic temporal changes in host susceptibility to infection: demographic mechanisms.

Simple mathematical models are developed to examine the influence of variability in host susceptibility to infection, on the dynamics of host-parasite population interactions. When hosts differ in their innate susceptibility (at birth), to infection by a specific parasite, the average susceptibility of the host population as a whole may show systematic changes through time. Such patterns may arise as a result of demographic factors associated with the interaction between host and parasite populations, in the absence of inheritance mechanisms (a genetic component) or acquired resistance (an immunological component). The general significance of this observation is discussed in terms of the coevolution of host-parasite associations.

Animals↗
PubMed2006

Reasoning under uncertainty: heuristic judgments in patients with persecutory delusions or depression.

OBJECTIVE: The substantial literature examining social reasoning in people with delusions has, to date, neglected the commonest form of decision making in daily life. We address this imbalance by reporting here the findings of the first study to explore heuristic reasoning in people with persecutory delusions. METHOD: People with active or remitted paranoid delusions, depressed and healthy adults performed two novel heuristic reasoning tasks that varied in emotional valence. RESULTS: The findings indicated that people with persecutory delusions displayed biases during heuristic reasoning that were most obvious when reasoning about threatening and positive material. Clear similarities existed between the currently paranoid group and the depressed group in terms of their reasoning about the likelihood of events happening to them, with both groups tending to believe that pleasant things would not happen to them. However, only the currently paranoid group showed an increased tendency to view other people as threatening. CONCLUSION: This study has initiated the exploration of heuristic reasoning in paranoia and depression. The findings have therapeutic utility and future work could focus on the differentiation of paranoia and depression at a cognitive level.

Adult↗
PubMed2006

The occurrence of organochlorines in marine avian top predators along a latitudinal gradient.

The aim of this study was to determine the role of cold condensation and fractionation on the occurrence of organochlorine contaminants (OCs) in avian marine top predators along a latitudinal gradient. We measured 24 polychlorinated biphenyl (PCB) congeners and six pesticide OCs in blood of great black-backed gulls (Larus marinus) from the Norwegian Coast (58 degrees N-70 degrees N) and glaucous gulls (Larus hyperboreus) from Bjornoya in the Norwegian Arctic (74 degrees N). Glaucous gulls had up to 3 times higher sigmaOC concentrations compared to the great black-backed gulls, and a OC pattern dominated largely by persistent and low volatile compounds such as highly chlorinated PCBs and metabolites such as oxychlordane. This was not consistent with cold condensation and fractionation theory, but probably related to diet and elevated biomagnification. Among great black-backed gulls, however, there were indications of both cold condensation and fractionation. Higher and lower chlorinated PCBs had highest absolute concentrations in the south and in the north, respectively, except for one location at an intermediate latitude, where concentrations of most OCs exceeded all other locations. In terms of proportional contribution to sigmaOC (pattern), relatively volatile OCs such as HCB, oxychlordane and tri- to penta- PCB congeners were more important at northern latitudes, while hexa- to nona-PCBs made up a larger proportion of sigmaOC in the south. The results thus showed that differences in global distribution of compounds with different physicochemical properties could be detected in avian top predators such as large gulls, even if biomagnification and biotransformation influence both the absolute concentrations and the patterns of OCs.

Animals↗
PubMed1999

Estimating risk assessment exposure point concentrations when the data are not normal or lognormal.

The U.S. Environmental Protection Agency (EPA) recommends the use of the one-sided 95% upper confidence limit of the arithmetic mean based on either a normal or lognormal distribution for the contaminant (or exposure point) concentration term in the Superfund risk assessment process. When the data are not normal or lognormal this recommended approach may overestimate the exposure point concentration (EPC) and may lead to unecessary cleanup at a hazardous waste site. The EPA concentration term only seems to perform like alternative EPC methods when the data are well fit by a lognormal distribution. Several alternative methods for calculating the EPC are investigated and compared using soil data collected from three hazardous waste sites in Montana, Utah, and Colorado. For data sets that are well fit by a lognormal distribution, values for the Chebychev inequality or the EPA concentration term may be appropriate EPCs. For data sets where the soil concentration data are well fit by gamma distributions, Wong's method may be used for calculating EPCs. The studentized bootstrap-t and Hall's bootstrap-t transformation are recommended for EPC calculation when all distribution fits are poor. If a data set is well fit by a distribution, parametric bootstrap may provide a suitable EPC.

Evaluation Studies as Topic↗
PubMed1999

Conditional uncertainty analysis and implications for decision making: the case of WIPP.

Uncertainty analyses and the reporting of their results can be misinterpreted when these analyses are conditional on a set of assumptions generally intended to bring some conservatism in the decisions. In this paper, two cases of conditional uncertainty analysis are examined. The first case includes studies that result, for instance, in a family of risk curves representing percentiles of the probability distribution of the future frequency of exceeding specified consequence levels conditional on a set of hypotheses. The second case involves analyses that result in an interval of outcomes estimated on the basis of conservative assumptions. Both types of results are difficult to use because they are sometimes misinterpreted as if they represented the output of a full uncertainty analysis. In the first case, the percentiles shown on each risk curve may be taken at face value when in reality (in marginal terms) they are lower if the chosen hypotheses are conservative. In the second case, the fact that some segments of the resulting interval are highly unlikely--or that some more benign segments outside the range of results are quite possible--does not appear. Also, these results are difficult to compare to those of analyses of other risks, possibly competing for the same risk management resources, and the decision criteria have to be adapted to the conservatism of the hypotheses. In this paper, the focus is on the first type (conditional risk curves) more than on the second and the discussion is illustrated by the case of the performance assessment of the Waste Isolation Pilot Plant in New Mexico. For policy-making purposes, however, the problems of interpretation, comparison, and use of the results are similar.

Confidence Intervals↗
PubMed2000

Assessment of molecular structure using frame-independent orientational restraints derived from residual dipolar couplings.

Residual dipolar couplings measured in weakly aligning liquid-crystalline solvent contain valuable information on the structure of biomolecules in solution. Here we demonstrate that dipolar couplings (DCs) can be used to derive a comprehensive set of pairwise angular restraints that do not depend on the orientation of the alignment tensor principal axes. These restraints can be used to assess the agreement between a trial protein structure and a set of experimental dipolar couplings by means of a graphic representation termed a 'DC consistency map'. Importantly, these maps can be used to recognize structural elements consistent with the experimental DC data and to identify structural parameters that require further refinement, which could prove important for the success of DC-based structure calculations. This approach is illustrated for the 42 kDa maltodextrin-binding protein.

Algorithms↗
PubMed1979

Auditory phase and frequency discrimination: a comparison of nine procedures.

Two auditory discrimination tasks were thoroughly investigated: discrimination of frequency differences from a sinusoidal signal of 200 Hz and discrimination of differences in relative phase of mixed sinusoids of 200 Hz and 400 Hz. For each task psychometric functions were constructed for three observers, using nine different psychophysical measurement procedures. These procedures included yes-no, two-interval forced-choice, and various fixed- and variable-standard designs that investigators have used in recent years. The data showed wide ranges of apparent sensitivity. For frequency discrimination, models derived from signal detection theory for each psychophysical procedure seem to account for the performance differences. For phase discrimination the models do not account for the data. We conclude that for some discriminative continua the assumptions of signal detection theory are appropriate, and underlying sensitivity may be derived from raw data by appropriate transformations. For other continua the models of signal detection theory are probably inappropriate; we speculate that phase might be discriminable only on the basis of comparison or change and suggest some tests of our hypothesis.

Dominance, Cerebral↗
PubMed1991

Recognition failure and the composite memory trace in CHARM.

The relation between recognition and recall, and especially the orderly recognition-failure function relating recognition and the recognizability of recallable words, was investigated using a composite holographic associative recall-recognition memory model (CHARM). Ten series of computer simulations are presented. Analysis of CHARM and comparisons to other models indicate that the recognition-failure function depends on (a) both recognition and recall being similar (convolution-correlation) processes such that an interpretable representation is retrieved in both tasks and (b) the information underlying both recall and recognition being stored in the same composite memory trace. It is of considerable interest that constructs central to the distributed nature of CHARM are responsible for the model's adherence to the recognition-failure function.

Attention↗