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Trends in epidemiology in the 21st century: time to adopt Bayesian methods

Martinez,Edson Zangiacomi; Achcar,Jorge Alberto
Fonte: Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz Publicador: Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2014 EN
Relevância na Pesquisa
46.58%
2013 marked the 250th anniversary of the presentation of Bayes’ theorem by the philosopher Richard Price. Thomas Bayes was a figure little known in his own time, but in the 20th century the theorem that bears his name became widely used in many fields of research. The Bayes theorem is the basis of the so-called Bayesian methods, an approach to statistical inference that allows studies to incorporate prior knowledge about relevant data characteristics into statistical analysis. Nowadays, Bayesian methods are widely used in many different areas such as astronomy, economics, marketing, genetics, bioinformatics and social sciences. This study observed that a number of authors discussed recent advances in techniques and the advantages of Bayesian methods for the analysis of epidemiological data. This article presents an overview of Bayesian methods, their application to epidemiological research and the main areas of epidemiology which should benefit from the use of Bayesian methods in coming years.

Medical Diagnosis Using Bayes Theorem

Lincoln, Thomas L.; Parker, Rodger D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em //1967 EN
Relevância na Pesquisa
46.28%
A computerized study of the applicability of Bayes theorem to the differential diagnosis of liver disease has been made. Statistical independence of symptoms is not presumed. The semantic obstacle involved in precise definition of the symptom and disease categories is discussed. Input for the study was obtained from patient records, and diagnosis supported by tissue examination, either at autopsy or by biopsy. Correct diagnosis rate is considered sufficiently high to warrant further investigation.

Personalization Through the Application of Inverse Bayes to Student Modeling

Lang, Charles WM
Fonte: Harvard University Publicador: Harvard University
Tipo: Thesis or Dissertation; text Formato: application/pdf
EN
Relevância na Pesquisa
36.37%
Personalization, the idea that teaching can be tailored to each students’ needs, has been a goal for the educational enterprise for at least 2,500 years (Regian, Shute, & Shute, 2013, p.2). Recently personalization has picked up speed with the advent of mobile computing, the Internet and increases in computer processing power. These changes have begun to generate more and more information about individual students that could theoretically be used to power personalized education. The following dissertation discusses a novel algorithm for processing this data to generate estimates of individual level attributes, the Inverse Bayes Filter (IBFi). A brief introduction to the use of Bayes Theorem is followed by a theoretical chapter and then two empirical chapters that describe alternately how the model is constructed, and how it performs on real student data. The theoretical chapter presents both the theory behind Inverse Bayes, including subjective probability, and then relates this theory to student performance. The first empirical chapter describes the prediction accuracy of IBFi on two proxies for students’ subjective probability, partial credit and cumulative average. This prediction performance is compared to the prediction accuracy of a modified Bayesian Knowledge Tracing model (KTPC) with IBFi performing reasonably...

Improving Multi-class Text Classification with Naive Bayes

Rennie, Jason D. M.
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Formato: 49 p.; 2017370 bytes; 687421 bytes; application/postscript; application/pdf
EN_US
Relevância na Pesquisa
36.55%
There are numerous text documents available in electronic form. More and more are becoming available every day. Such documents represent a massive amount of information that is easily accessible. Seeking value in this huge collection requires organization; much of the work of organizing documents can be automated through text classification. The accuracy and our understanding of such systems greatly influences their usefulness. In this paper, we seek 1) to advance the understanding of commonly used text classification techniques, and 2) through that understanding, improve the tools that are available for text classification. We begin by clarifying the assumptions made in the derivation of Naive Bayes, noting basic properties and proposing ways for its extension and improvement. Next, we investigate the quality of Naive Bayes parameter estimates and their impact on classification. Our analysis leads to a theorem which gives an explanation for the improvements that can be found in multiclass classification with Naive Bayes using Error-Correcting Output Codes. We use experimental evidence on two commonly-used data sets to exhibit an application of the theorem. Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification.

A probabilistic approach for the evaluation of minimal residual disease by multiparameter flow cytometry in leukemic B-cell chronic lymphoproliferative disorders; Cytometry Part A

Pedreira, C. E.; Costa, E. S.; Quijano, S.; Almeida, J.; Fernandez, C.; Florez, J.; Barrena, S.; Lecrevisse, Q.; Van Dongen, J. J. M.; Orfao, A.
Fonte: Pontifícia Universidade Javeriana Publicador: Pontifícia Universidade Javeriana
Formato: 1141-1150
Relevância na Pesquisa
46.28%
Vol. 73A, No. 12; Multiparameter flow cytometry has become an essential tool for monitoring response to therapy in hematological malignancies, including B-cell chronic lymphoproliferative disorders (B-CLPD). However, depending on the expertise of the operator minimal residual disease (MRD) can be misidentified, given that data analysis is based on the definition of expert-based bidimensional plots, where an operator selects the subpopulations of interest. Here, we propose and evaluate a probabilistic approach based on pattern classification tools and the Bayes theorem, for automated analysis of flow cytometry data from a group of 50 B-CLPD versus normal peripheral blood B-cells under MRD conditions, with the aim of reducing operator-associated subjectivity. The proposed approach provided a tool for MRD detection in B-CLPD by flow cytometry with a sensitivity of 8 3 1025 (median of 2 3 1027). Furthermore, in 86% of BCLPD cases tested, no events corresponding to normal B-cells were wrongly identified as belonging to the neoplastic B-cell population at a level of 1027. Thus, this approach based on the search for minimal numbers of neoplastic B-cells similar to those detected at diagnosis could potentially be applied with both a high sensitivity and specificity to investigate for the presence of MRD in virtually all B-CLPD. Further studies evaluating its efficiency in larger series of patients...

Dificultades de los estudiantes de Psicolog??a en el c??lculo de probabilidades inversas mediante el Teorema de Bayes

D??az Batanero, Carmen; Ortiz, Juan J.; Serrano, Luis
Fonte: Universidad de Granada Publicador: Universidad de Granada
Tipo: Artigo de Revista Científica
SPA
Relevância na Pesquisa
46.51%
En este trabajo realizamos un an??lisis te??rico de los pasos necesarios en el c??lculo de probabilidades inversas por medio del teorema de Bayes y presentamos un estudio emp??rico de errores en una muestra de 414 estudiantes de Psicolog??a, despu??s de la ense??anza del tema. Presentamos tambi??n los resultados de una expe??riencia de ense??anza del teorema de Bayes y sus aplicaciones a 78 alumnos de Psico??log??a, apoyada en Excel, dirigida a superar las dificultades descritas. Los resultados indicaron la consecuci??n de los objetivos did??cticos en la mayor??a de los alumnos participantes; In this paper we present a theoretical analysis of the steps needed to compute probabilities in applying the Bayes' theorem. Then, we present an empirical study of difficulties and mistakes in a sample of 414 Psychology students, after instruction. We also describe a teaching experience of the Bayes theorem in a sample of 78 students supported by Excel and aimed to overe??me the described difficulties. Results suggested that didactical objectives were achieved by the most of the students who took part in the experience.

Regression Analysis of Hierarchical Poisson-like Event Rate Data: Superpopulation Model Effect on Predictions

Gaver, Donald Paul; O'Muircheartaigh, I. G.; Jacobs, Patricia A.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Relatório
EN_US
Relevância na Pesquisa
36.26%
This paper studies prediction of future failure (rates) by hierarchical empirical Bayes (EB) Poisson regression methodologies. Both a gamma distributed super-population as well as a more robust (long-tailed) log student- t super-population are considered. Simulation results are reported concerning predicted Poisson rates. The results tentatively suggest that a hierarchical model with gamma super-population can effectively adapt to data coming from a log-Student-t-super-population particularly if the additional computation involved with estimation for the log-Student-t hierarchical model is burdensome; Naval Postgraduate School Research Council Research Program.; http://archive.org/details/regressionanalys00gave; O&MN, Direct Funding

A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

Xu, Gaochao; Ding, Yan; Zhao, Jia; Hu, Liang; Fu, Xiaodong
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Publicado em 09/12/2013 EN
Relevância na Pesquisa
46.28%
Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

Bayes-based confidence measure in speech recognition

Carrasco, Jorge; Molina, Carlos; Becerra Yoma, Néstor
Fonte: Universidade do Chile Publicador: Universidade do Chile
Tipo: Artículo de revista
EN_US
Relevância na Pesquisa
46.26%
Artículo de publicación ISI; In this letter, Bayes-based confidence measure (BBCM) in speech recognition is proposed. BBCM is applicable to any standard word feature and makes use of information about the speech recognition engine performance. In contrast to ordinary confidence measures, BBCM is a probability, which is interesting itself from the practical and theoretical point of view. If applied with word density confidence measure (WDCM), BBCM dramatically improves the discrimination ability of the false acceptance curve when compared to WDCM itself.

The central role of Bayes theorem for joint estimation of causal effects and propensity scores

Zigler, Corwin M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.47%
Although propensity scores have been central to the estimation of causal effects for over 30 years, only recently has the statistical literature begun to consider in detail methods for Bayesian estimation of propensity scores and causal effects. Underlying this recent body of literature on Bayesian propensity score estimation is an implicit discordance between the goal of the propensity score and the use of Bayes theorem. The propensity score condenses multivariate covariate information into a scalar to allow estimation of causal effects without specifying a model for how each covariate relates to the outcome. Avoiding specification of a detailed model for the outcome response surface is valuable for robust estimation of causal effects, but this strategy is at odds with the use of Bayes theorem, which presupposes a full probability model for the observed data. The goal of this paper is to explicate this fundamental feature of Bayesian estimation of causal effects with propensity scores in order to provide context for the existing literature and for future work on this important topic.

Alternatives to the neoBayesian Theorem, avoiding several of its inconsistencies: The rMPE-Method

Gottlob, Rainer
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.36%
Some drawbacks of the formalism of Bayes Theorem can be avoided by the rMPE-Method, a modification of the cMPE-Method that permits (i): Adding probabilities in spite of non-linearity. (ii): Taking into account extensional evidence and weight-bearing evidence that are mutually dependent, but opposed in their effects. (iii): Arriving at higher probabilities than by Bayes Theorem and (iv): Confirming also hypotheses that imply certain evidence.; Comment: 14 pagwes, 6 figures Replaced for improved clarity and precision

Bayes and Naive Bayes Classifier

Vikramkumar; B, Vijaykumar; Trilochan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/04/2014
Relevância na Pesquisa
36.47%
The Bayesian Classification represents a supervised learning method as well as a statistical method for classification. Assumes an underlying probabilistic model and it allows us to capture uncertainty about the model in a principled way by determining probabilities of the outcomes. This Classification is named after Thomas Bayes (1702-1761), who proposed the Bayes Theorem. Bayesian classification provides practical learning algorithms and prior knowledge and observed data can be combined. Bayesian Classification provides a useful perspective for understanding and evaluating many learning algorithms. It calculates explicit probabilities for hypothesis and it is robust to noise in input data. In statistical classification the Bayes classifier minimises the probability of misclassification. That was a visual intuition for a simple case of the Bayes classifier, also called: 1)Idiot Bayes 2)Naive Bayes 3)Simple Bayes

How Information Transfer works: interpretation of Information Contents in Bayes Theorem. Understanding Negative Information

Lopez-Medrano, Alvaro
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.4%
In a given space of models or hypothesis the individual information content of each of them is considered as opposed to the Shannon entropy that measures the average information content of the mentioned space. In particular expressing Bayes Theorem in terms of the information contents associated to its probabilities allows understanding how bits of information, introduced in the system by an observation, are transferred to each of the models in the space. It is shown how, from a single observation not one, but two causal information sources are generated: the Information Content Associated to the Evidence that always introduces positive information, and the Information Content Associated to the Bayes Likelihood that always introduces negative bits; therefore the evidence contributes to increase the probability of occurrence of the model and the likelihood to decrease it; depending on the net value of the difference between these two mentioned information contents, the information that arrives to a given model will be positive or negative. Thus, we propose a novel metric, given by the difference of the two mentioned information contents called transfer information content which measures the information transferred to each of the single models in the space. The resolution of the Monty Hall Problem (MHP) and some of its variants in the Information Theory framework proposed allows to confirm the validity of the formulas derived and to understand the counterintuitive and theoretically problematic concept of negative information. The implications of the concepts introduced in terms of information transfer to the emergent field of Local Information Dynamics...

Is Bayes theorem applicable to all quantum states?

Razmi, H.; Allahyari, J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.28%
Reconsidering the already known important question that whether all the axioms and theorems in classical theory of probability are applicable to probability functions in quantum theory, we want to show that the so-called Bayes theorem isn't applicable to nonfactorizable quantum entangled states.; Comment: 6 pages, Accepted for publication in Iranian Journal of Science and Technology A

Posterior probability and fluctuation theorem in stochastic processes

Ohkubo, Jun
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 29/09/2009
Relevância na Pesquisa
36.24%
A generalization of fluctuation theorems in stochastic processes is proposed. The new theorem is written in terms of posterior probabilities, which are introduced via the Bayes theorem. In usual fluctuation theorems, a forward path and its time reversal play an important role, so that a microscopically reversible condition is essential. In contrast, the microscopically reversible condition is not necessary in the new theorem. It is shown that the new theorem adequately recovers various theorems and relations previously known, such as the Gallavotti-Cohen-type fluctuation theorem, the Jarzynski equality, and the Hatano-Sasa relation, when adequate assumptions are employed.; Comment: 4 pages

Bayes Theorem and the cMPE-Method: Differences, Complementarities and the Importance of Discerning between Weight-bearing and Extensional Evidence

Gottlob, R.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.47%
Both, Bayes Theorem and the cMPE-Method serve for establishing relations between systems of probabilities. By the cMPE-Method non-conditional probabilities are added, by the DPE-Method, they are subtracted, however, in both versions allowing for the non-linearity of non-disjunctive probabilities. Semantic independence is prerequisite. As compared with the results of semantically homogeneous series of observations, the variety of evidence permits arrival at higher probabilities. The advantage of the Bayesian method lies in allowing for evidence extraneous to the domain covered by the hypothesis. We must differentiate between extensional and weight-bearing evidence. Operations based on purely weight-bearing evidence (cMPE-Method) neglect the extensional evidence and some operations according to Bayes Theorem may neglect weight-bearing evidence at least partially. These and some other shortcomings may be remedied by operations, combining both of the approaches.; Comment: 16 pages 7 figures

The Fermi's Bayes Theorem

D'Agostini, G.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 10/09/2005
Relevância na Pesquisa
46.58%
It is curious to learn that Enrico Fermi knew how to base probabilistic inference on Bayes theorem, and that some influential notes on statistics for physicists stem from what the author calls elsewhere, but never in these notes, {\it the Bayes Theorem of Fermi}. The fact is curious because the large majority of living physicists, educated in the second half of last century -- a kind of middle age in the statistical reasoning -- never heard of Bayes theorem during their studies, though they have been constantly using an intuitive reasoning quite Bayesian in spirit. This paper is based on recollections and notes by Jay Orear and on Gauss' ``Theoria motus corporum coelestium'', being the {\it Princeps mathematicorum} remembered by Orear as source of Fermi's Bayesian reasoning.; Comment: 4 pages, to appear in the Bulletin of the International Society of Bayesian Analysis (ISBA). Related links and documents are available in http://www.roma1.infn.it/~dagos/history/

Bayes linear spaces

Van Den Boogart, Karl-Gerald; Egozcue, Juan José; Pawlowsky-Glahn, Vera
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2010 ENG
Relevância na Pesquisa
36.26%
Linear spaces consisting of o-finite probability measures and infinite measures (improper priors and likelihood functions) are defined. The commutative group operation, called perturbation, is the updating given by Bayes theorem; the inverse operation is the Radon-Nikodym derivative. Bayes spaces of measures are sets of classes of proportional measures. In this framework, basic notions of mathematical statistics get a simple algebraic interpretation. For example, exponential families appear as affine subspaces with their sufficient statistics as a basis. Bayesian statistics, in particular some well-known properties of conjugated priors and likelihood functions, are revisited and slightly extended.

El planteamiento de problemas y la construcción del Teorema de Bayes

Penalva Martínez, M. Carmen; Posadas García, José Adolfo
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2009 SPA
Relevância na Pesquisa
36.54%
Las tareas de resolución y planteamiento de problemas posibilitan indagar sobre aprendizajes específicos de los estudiantes. Plantear un problema significa idear un problema preconcibiendo un plan para su resolución. En la actualidad, sabemos poco de los procesos cognitivos usados por los estudiantes que pueden ser efectivos para plantear problemas de matemáticas. Para dar cuenta de cómo estudiantes universitarios construyen conocimiento relativo al teorema de Bayes cuando realizan tareas de planteamiento de problemas, usamos el modelo de la abstracción en contexto de acciones epistémicas anidadas de Hershkowitz, Schwarz y Dreyfus (2001). Los resultados muestran características distintas en el proceso de construcción del teorema de Bayes que ponen de manifiesto el potencial de las tareas de plantear problemas para desencadenar procesos de abstracción.; Problem posing and problem solving are suitable tasks for inquiring into the learning of specific concepts. Posing a problem involves setting up a text for the problem and thinking about a plan to solve it. Currently, we have scarce knowledge about students’ effective cognitive process when posing mathematical problems. We have used the theoretical framework of nested epistemic actions (Hershkowitz...

Trends in epidemiology in the 21st century: time to adopt Bayesian methods

Martinez,Edson Zangiacomi; Achcar,Jorge Alberto
Fonte: Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz Publicador: Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2014 EN
Relevância na Pesquisa
46.58%
2013 marked the 250th anniversary of the presentation of Bayes’ theorem by the philosopher Richard Price. Thomas Bayes was a figure little known in his own time, but in the 20th century the theorem that bears his name became widely used in many fields of research. The Bayes theorem is the basis of the so-called Bayesian methods, an approach to statistical inference that allows studies to incorporate prior knowledge about relevant data characteristics into statistical analysis. Nowadays, Bayesian methods are widely used in many different areas such as astronomy, economics, marketing, genetics, bioinformatics and social sciences. This study observed that a number of authors discussed recent advances in techniques and the advantages of Bayesian methods for the analysis of epidemiological data. This article presents an overview of Bayesian methods, their application to epidemiological research and the main areas of epidemiology which should benefit from the use of Bayesian methods in coming years.