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PubMed1997

[Explanation and estimation of subjective probability by generalized model of Support Theory].

The purpose of this paper is to develop a new model for describing the cognitive process of decision making. Being of a generalization of Tversky and Koehler's Support Theory, the proposed model firstly defines the choice probability among several alternatives in terms of degree of "support." Secondly the model defines the degree of support in terms of objective probability and representativeness. Thirdly, this model specifies the integration process by which one reaches the probability of an event utilizing the already assessed subevents. This model was applied to and tasted by, real experimental data. In the experiment, subjects were asked to evaluate the proportions (a kind of probability) and the degrees of representativeness of two objects. Compared to the estimates derived by a Bayesian approach, the subjective probabilities estimated by the proposed model were shown to be closer to those reported by the subjects.

Adult↗
PubMed1986

Theory of psychiatric treatment systems. An approach.

A theory of psychiatric treatment systems may be approached using stochastic probability models. Theory construction proceeds in six stages corresponding to broad assumptions about the causal relationships among variables affecting the life course and treatment encounters of members of a target population. The theory is progressively refined to describe short-term life changes, longer-term life-course modifiers, predictors of treatment utilization, treatment system policies, and treatment effects. The ultimate aim is to forecast the effects of policy change.

Health Policy↗
PubMed2004

Fast iterative gene clustering based on information theoretic criteria for selecting the cluster structure.

Grouping of genes into clusters according to their expression levels is important for deriving biological information, e.g., on gene functions based on microarray and other related analyses. The paper introduces the selection of the number of clusters based on the minimum description length (MDL) principle for the selection of the number of clusters in gene expression data. The main feature of the new method is the ability to evaluate in a fast way the number of clusters according to the sound MDL principle, without exhaustive evaluations over all possible partitions of the gene set. The estimation method can be used in conjunction with various clustering algorithms. A recent clustering algorithm using principal component analysis, the "gene shaving" (GS) procedure, can be modified to make use of the new MDL estimation method, replacing the Gap statistics originally used in GS algorithm. The resulting clustering algorithm is shown to perform better than GS-Gap and CEM (classification expectation maximization), in the simulations using artificial data. The proposed method is applied to B-cell differentiation data, and the resulting clusters are compared with those found by self-organizing maps (SOM).

Algorithms↗
PubMed1997

Unpacking, repacking, and anchoring: advances in support theory.

Support theory represents probability judgment in terms of the support, or strength of evidence, of the focal relative to the alternative hypothesis. It assumes that the judged probability of an event generally increases when its description is unpacked into disjoint components (implicit subadditivity). This article presents a significant extension of the theory in which the judged probability of an explicit disjunction is less than or equal to the sum of the judged probabilities of its disjoint components (explicit subadditivity). Several studies of probability and frequency judgment demonstrate both implicit and explicit subadditivity. The former is attributed to enhanced availability, whereas the latter is attributed to repacking and anchoring.

Humans↗
PubMed2003

The effects of control on betting: paradoxical betting on items of high confidence with low value.

Traditional decision theories emphasize the probabilities and values of possible outcomes, but decisions may also be influenced by perceived control, with control defined as probability alterability. In 3 experiments, participants were offered bets on their own answers to general knowledge questions, bets that are characterized by control. The bets were fair if participants' reported confidence was well calibrated, positively valued if participants were underconfident, but unfavorable when participants were overconfident. Bet acceptance was a steep, linear, increasing function of confidence that is termed paradoxical betting. This pattern was generally contrary to the value of bets (considered either as average outcome or as subjective utility) and was steeper in slope than matched bets on apparently random events in Experiment 3. The author argues that control is a fundamental determinant of decision making that is readily incorporated in some existing models of decision weighting.

Confidence Intervals↗
PubMed1982

The eventual frequencies of kin in a stable population.

Associated with every real birth cohort of women is a set of probabilities [fk] of eventually having k daughters. With a variant of stable population theory, these probabilities are used to generate the entire probability distributions, as well as all moments, for all categories of skin who are female and female-related. With additional assumptions, a full two-sex model for all kin also is given. The two-sex model is applied to a cohort of U.S. women born in the mid-twentieth century, suggesting plausible frequencies of kin in a stationary population.

Adult↗
PubMed2006

Chromatography as Lévy stochastic process.

The Stochastic Theory of Chromatography has been revised in light of some of the most relevant Lévy's findings in Theory of Probability, including the so-called Lévy's distance, the characteristic function and the theory of infinitesimally divisible distributions. These concepts represent the key to exploit and understand, at a molecular basis, phenomena typical of chromatographic separations under linear conditions, such as peak tailing and splitting. In particular, Lévy's distance has been used to quantify the degree of convergence of real peaks towards an ideal Gaussian shape; the characteristic function properties, introduced by Lévy to deal with the problem of the addition of independent random variables, have been employed to solve a wide variety of chromatographic models (including adsorption on heterogeneous surfaces) and to interpret mobile phase dispersion from a probabilistic point of view. Finally, Lévy's studies concerning infinitesimally divisible distributions have allowed to introduce in the stochastic description of chromatography, effects associated to dispersion in mobile phase. It has been demonstrated that, according to Lévy's canonical representation of stochastic processes, the basis of chromatography is a mobile phase Poisson Process. Represented as a Lévy's process, the microscopic-probabilistic model of chromatography permits the establishment of a connection between single-molecule properties and their statistical fluctuations and shapes of real chromatographic peaks allowing, at the same time, for the constitution of a link between different branches of physical sciences.

Chromatography↗
PubMed2006

Probability-based differential normalized fluorescence bivariate analysis for the classification of tissue autofluorescence spectra.

Differential normalized fluorescence (DNF) is an efficient and effective method for the differentiation of normal and cancerous tissue fluorescence spectra. The diagnostic features are extracted from the difference between the averaged cancerous and averaged normal tissue spectra and used as indices in tissue classification. In this paper, a new method, probability-based DNF bivariate analysis, is introduced based on the univariate DNF method. Two differentiation features are used concurrently in the new method to achieve better classification accuracy. The probability of each sample belonging to a disease state is determined with Bayes decision theory. This probability approach classifies the tissue spectra according to disease states and provides uncertainty information on classification. With a data set of 57 colonic tissue sites, probability-based DNF bivariate analysis is demonstrated to improve the accuracy of cancer diagnosis. The bivariate DNF analysis only requires the collection of a few data points across the entire emission spectrum and has the potential of improving data acquisition speed in tissue imaging.

Analysis of Variance↗
PubMed1996

Direct measurement of ion distributions between lipid membranes with X-ray diffraction.

A new and simple method is introduced, which allows the direct measurement of the distribution of ions between lipid membranes with a conventional X-ray source. It is based on a difference method which is combined with a swelling experiment. The presented method is applied to unoriented powder samples of 1,2-dipalmitoyl-sn-glycero-3-phosphorylglycerol in different ionic solutions of RbCl and BaCl2. From these samples, results for the cation distributions with a resolution of 12 A degrees were obtained. Analysis of the experimentally obtained distributions shows that the simple Gouy-Chapman theory is probably not able to describe the experimental data consistently. Instead a better correspondence between experiment and theory is obtained with a generalized linear Gouy-Chapman model which takes into account the finite width of the lipid/electrolyte interface. Possible future improvements of the presented method with regard to the obtained resolution and the possibility to obtain ion densities on an absolute scale are discussed.

Barium Compounds↗
PubMed2002

Cryptosporidium dose-response studies: variation between hosts.

The issue of variation is highly important in dose-response analysis: variation among genetically related pathogens infecting the same host, but also variation among hosts, in susceptibility to infection by the same pathogen. This latter issue is addressed here for the protozoan parasite Cryptosporidium parvum, the causative agent for many outbreaks of water-borne gastrointestinal illness. In human feeding studies, infectivity has been shown to be low in subjects with high preexisting anti-Cryptosporidium IgG-levels. Here we adapt the hit theory model of microbial infection to incorporate covariables, characterizing the immune status of the susceptible host. The probability of any single oocyst in the inoculum to cause infection appears to depend on preexisting IgG-levels. This does not necessarily imply direct protection by the humoral immune system; high IgG-levels may reflect a recent episode of infection/illness, and be an epi-phenomenon associated with other protective responses. The IgG-dependence of the dose-response relation can be easily applied in quantitative risk analysis. The distribution of anti-Cryptosporidium IgG levels in the general population is accessible by analyzing serum banks, which are maintained in many Western countries. Using such an approach provides first insights into the variation of susceptibility to infection in the general population.

Animals↗
PubMedDate not supplied

[Causality in urologic research].

Clinical-epidemiological research may orient us about the causes of disease, the relationships among them, and the relative magnitudes of their effects. The objective of this article is to link the notion of cause with the basic clinical-epidemiological parameters. There are different models explaining causality. All of them present the possible etiologic explanations for the diseases, taking into consideration the current knowledge at the time they have been posed. We start from a purely determinist conception, understanding causality as a constant connection between two factors x and y, unique, and perfectly predictable. Currently, this model is inadequate to be applied to many diseases. Many researchers have modified the determinist model to explain the multiple causality of disease, posing the existence of associations of causal factors, more than single factors, being these associations treated as sufficient cause (i.e. as a group of minimal conditions and events that inevitably produce the disease). That determinist concept of causality is supplemented with the probabilistic concept. The theory of probability is used in it, as well as the related statistical, methods, to empirically evaluate a possible association that is believed causal. As a consequence of the lack of certainty of the prediction at the individual level, the theoretical notion of cause is replaced by the empirical concept of risk factor, referring to a variable which is considered to be related to the probability that one individual develops the disease. Causal inference in epidemiology is the logic development of a theory, based on observations and arguments that attribute the presence (association) of a disease to one or more risk factors. We will follow the principles posed by B. Hill for the complex process called scientific generalization. To correctly perform this relationship between our ideas and are observations it is absolutely important to start from a correct election of the study design with which the research is undertaken.

Biomedical Research↗
PubMed2000

Subjective uncertainty, purposeful behavior, and theory of functional systems.

Neglect of probability prognosis orients the theory of functional systems to description of rigidly determined forms of behavior (congenital behavior, frontal syndrome, etc.). The authors suggest an original concept for analysis of behavior under conditions of subjective uncertainty based on probability estimations. It contains all classical components of a behavioral act model developed by P. K. Anokhin. The role of probability prognosis at the stages of making decision (purpose) and formation and realization of the program of actions is argued. Two new components are added to the classical model: memory buffer and system of making probabilistic decision for modifying the program of actions. Such an approach essentially extends the potentialities of using the theory of functional systems, making it usable not only for rigidly determined behavior but for behavior under conditions of subjective uncertainty.

Animals↗
PubMed2005

Quantifying stimulus discriminability: a comparison of information theory and ideal observer analysis.

Performance in sensory discrimination tasks is commonly quantified using either information theory or ideal observer analysis. These two quantitative frameworks are often assumed to be equivalent. For example, higher mutual information is said to correspond to improved performance of an ideal observer in a stimulus estimation task. To the contrary, drawing on and extending previous results, we show that five information-theoretic quantities (entropy, response-conditional entropy, specific information, equivocation, and mutual information) violate this assumption. More positively, we show how these information measures can be used to calculate upper and lower bounds on ideal observer performance, and vice versa. The results show that the mathematical resources of ideal observer analysis are preferable to information theory for evaluating performance in a stimulus discrimination task. We also discuss the applicability of information theory to questions that ideal observer analysis cannot address.

Algorithms↗
PubMedDate not supplied

[Elements of probability calculation. I].

This is the first of a series of articles on probability calculation, consisting of a simple introduction to the definitions of probability. A brief historical background explains some of the reasons for the delay in the development of probability calculation and recalls the main problems that had to be faced, the solution of which marked the birth of the theory of probability. Classical, frequentist and subjective approaches are addressed and their relative merits and weaknesses are discussed.

Probability↗
PubMed1998

An information theoretic view of gapped and other alignments.

We use an information theoretical framework to estimate the probability of the score of gapped alignments. With appropriate scaling, the score of a global (and with some adjustments also the score of a local) alignment of two sequences can be viewed as the difference in the number of bits needed to transmit the two sequences T1 and T2 under two different encoding schemes C1 and C2. C1 is an idealized scheme, assumed to achieve an optimal encoding with respect to a distribution p, and the assumption that T1 and T2 are independent. C2 is an alternate scheme, that will transmit T1 and T2 while taking advantage of the optimal alignment between the two. That is under C1, the strings T1 and T2 (with respective probabilities p(T1) and p(T2)), are assumed to be encoded using C1(T1, T2) = log [formula: see text] bits. By slightly modifying a known Theorem we show that the probability (under p) that two independent sequences T1, T2 can be transmitted with an alternate encoding scheme (C2) with no more than C1(T1, T2)-r bits is bounded by 2-r. We then show how to use this bound to derive upper bounds for the probability of gapped alignment scores between two sequences.

Computer Simulation↗