235,788 research outputs found
Mathematical models of games of chance: Epistemological taxonomy and potential in problem-gambling research
Games of chance are developed in their physical consumer-ready form on the basis of mathematical models, which stand as the premises of their existence and represent their physical processes. There is a prevalence of statistical and probabilistic models in the interest of all parties involved in the study of gambling – researchers, game producers and operators, and players – while functional models are of interest more to math-inclined players than problem-gambling researchers. In this paper I present a structural analysis of the knowledge attached to mathematical models of games of chance and the act of modeling, arguing that such knowledge holds potential in the prevention and cognitive treatment of excessive gambling, and I propose further research in this direction
Statistical Agent Based Modelization of the Phenomenon of Drug Abuse
We introduce a statistical agent based model to describe the phenomenon of
drug abuse and its dynamical evolution at the individual and global level. The
agents are heterogeneous with respect to their intrinsic inclination to drugs,
to their budget attitude and social environment. The various levels of drug use
were inspired by the professional description of the phenomenon and this
permits a direct comparison with all available data. We show that certain
elements have a great importance to start the use of drugs, for example the
rare events in the personal experiences which permit to overcame the barrier of
drug use occasionally. The analysis of how the system reacts to perturbations
is very important to understand its key elements and it provides strategies for
effective policy making. The present model represents the first step of a
realistic description of this phenomenon and can be easily generalized in
various directions.Comment: 12 pages, 5 figure
Statistical models and the theory of hypothesis testing in medicine
Purpose: The purpose of this work is to develop statistical approaches for treatment efficiency analysis on a medical case. These methods include the effect evaluation method performed with various preparations in regards to several factors such as the patient‘s blood state and his/her biochemical values.
Design/Methodology/Approach: The results of this survey were divided into three groups: the first included the blood biochemistry values before the test, the second — an hour after the test, the the third — two hours after the test. When visually comparing the results of the analyses of the three groups, it was possible to assume that the load of FLC did not significantly affect the biochemistry of the patients' blood. To test this medical assumption, various statistical criteria of the theory of testing statistical hypotheses have been applied.
Findings: Statistical analysis of changes in the level of lipase and triglycerides in the biochemical analysis of the blood of patients with chronic pancreatitis after ingestion of food containing medium-chain fatty acids showed that there is no overall disruption in the functioning of the pancreas.
Practical implications: Clinical practice has shown that more than 85% of patients tolerated testing with medium chain fatty acids, not experiencing a painful abdominal symptom and other negative consequences of a violation of external secretion of the pancreas.
Originality/Value: In clinical practice, the Russian Academy of Medical Sciences for the first time took into account the results of a statistical analysis.peer-reviewe
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using probabilistic state-space models and Bayesian
smoothing. Unlike most existing methods that proceed with sequential tracking
of branches, we present an exploratory method, that is less sensitive to local
anomalies in the data due to acquisition noise and/or interfering structures.
The evolution of individual branches is modelled using a process model and the
observed data is incorporated into the update step of the Bayesian smoother
using a measurement model that is based on a multi-scale blob detector.
Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother,
which provides Gaussian density estimates of branch states at each tracking
step. We select likely branch seed points automatically based on the response
of the blob detection and track from all such seed points using the RTS
smoother. We use covariance of the marginal posterior density estimated for
each branch to discriminate false positive and true positive branches. The
method is evaluated on 3D chest CT scans to track airways. We show that the
presented method results in additional branches compared to a baseline method
based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in
Biomedical Image Analysis. MICCAI 2017. Quebec Cit
Chronic infection: punctuated interpenetration and pathogen virulence
We apply an information dynamics formalism to the Levens and Lewontin vision of biological interpenetration between a 'cognitive condensation' including immune function embedded in social and cultural structure on the one hand, and an established, highly adaptive, parasite population on the other. We iterate the argument, beginning with direct interaction between cognitive condensation and pathogen, then extend the analysis to second order 'mutator' mechanisms inherent both to immune function and to certain forms of rapid pathogen antigenic variability.
The methodology, based on the Large Deviations Program of applied probability, produces synergistic cognitive/adaptive 'learning plateaus' that represent stages of chronic infection, and, for human populations, is able to encompass the fundamental biological reality of culture omitted by other approaches.
We conclude that, for 'evolution machine' pathogens like HIV and malaria, simplistic magic bullet 'medical' drug, vaccine, or behavior modification interventions which do not address the critical context of overall living and working conditions may constitute selection pressures triggering adaptations in life history strategy resulting in marked increase of pathogen virulenc
Learning about a Categorical Latent Variable under Prior Near-Ignorance
It is well known that complete prior ignorance is not compatible with
learning, at least in a coherent theory of (epistemic) uncertainty. What is
less widely known, is that there is a state similar to full ignorance, that
Walley calls near-ignorance, that permits learning to take place. In this paper
we provide new and substantial evidence that also near-ignorance cannot be
really regarded as a way out of the problem of starting statistical inference
in conditions of very weak beliefs. The key to this result is focusing on a
setting characterized by a variable of interest that is latent. We argue that
such a setting is by far the most common case in practice, and we show, for the
case of categorical latent variables (and general manifest variables) that
there is a sufficient condition that, if satisfied, prevents learning to take
place under prior near-ignorance. This condition is shown to be easily
satisfied in the most common statistical problems.Comment: 15 LaTeX page
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