The Experts below are selected from a list of 26655 Experts worldwide ranked by ideXlab platform
Herbert Hoijtink - One of the best experts on this subject based on the ideXlab platform.
-
why bayesian psychologists should change the way they use the bayes factor
Multivariate Behavioral Research, 2016Co-Authors: Herbert Hoijtink, Pascal Van Kooten, Koenraad HulskerAbstract:ABSTRACTThe discussion following Bem’s (2011) psi research highlights that applications of the Bayes factor in psychological research are not without problems. The first problem is the omission to translate Subjective Prior knowledge into Subjective Prior distributions. In the words of Savage (1961): “they make the Bayesian omelet without breaking the Bayesian egg.” The second problem occurs if the Bayesian egg is not broken: the omission to choose default Prior distributions such that the ensuing inferences are well calibrated. The third problem is the adherence to inadequate rules for the interpretation of the size of the Bayes factor. The current paper will elaborate these problems and show how to avoid them using the basic hypotheses and statistical model used in the first experiment described in Bem (2011). It will be argued that a thorough investigation of these problems in the context of more encompassing hypotheses and statistical models is called for if Bayesian psychologists want to add a well-f...
-
bayesian model selection of informative hypotheses for repeated measurements
Journal of Mathematical Psychology, 2009Co-Authors: Joris Mulder, Irene Klugkist, Rens Van De Schoot, Wim Meeus, Maarten H W Selfhout, Herbert HoijtinkAbstract:When analyzing repeated measurements data, researchers often have expectations about the relations between the measurement means. The expectations can often be formalized using equality and inequality constraints between (i) the measurement means over time, (ii) the measurement means between groups, (iii) the means adjusted for time-invariant covariates, and (iv) the means adjusted for time-varying covariates. The result is a set of informative hypotheses. In this paper, the Bayes factor is used to determine which hypothesis receives most support from the data. A pivotal element in the Bayesian framework is the specification of the Prior. To avoid Subjective Prior specification, training data in combination with restrictions on the measurement means are used to obtain so-called constrained posterior Priors. A simulation study and an empirical example from developmental psychology show that this Prior results in Bayes factors with desirable properties.
Thomas R Fanshawe - One of the best experts on this subject based on the ideXlab platform.
-
an empirical investigation into the role of Subjective Prior probability in searching for potentially missing items
Royal Society Open Science, 2015Co-Authors: Thomas R FanshaweAbstract:There are many examples from the scientific literature of visual search tasks in which the length, scope and success rate of the search have been shown to vary according to the searcher's expectations of whether the search target is likely to be present. This phenomenon has major practical implications, for instance in cancer screening, when the prevalence of the condition is low and the consequences of a missed disease diagnosis are severe. We consider this problem from an empirical Bayesian perspective to explain how the effect of a low Prior probability, Subjectively assessed by the searcher, might impact on the extent of the search. We show how the searcher's posterior probability that the target is present depends on the Prior probability and the proportion of possible target locations already searched, and also consider the implications of imperfect search, when the probability of false-positive and false-negative decisions is non-zero. The theoretical results are applied to two studies of radiologists' visual assessment of pulmonary lesions on chest radiographs. Further application areas in diagnostic medicine and airport security are also discussed.
Koenraad Hulsker - One of the best experts on this subject based on the ideXlab platform.
-
why bayesian psychologists should change the way they use the bayes factor
Multivariate Behavioral Research, 2016Co-Authors: Herbert Hoijtink, Pascal Van Kooten, Koenraad HulskerAbstract:ABSTRACTThe discussion following Bem’s (2011) psi research highlights that applications of the Bayes factor in psychological research are not without problems. The first problem is the omission to translate Subjective Prior knowledge into Subjective Prior distributions. In the words of Savage (1961): “they make the Bayesian omelet without breaking the Bayesian egg.” The second problem occurs if the Bayesian egg is not broken: the omission to choose default Prior distributions such that the ensuing inferences are well calibrated. The third problem is the adherence to inadequate rules for the interpretation of the size of the Bayes factor. The current paper will elaborate these problems and show how to avoid them using the basic hypotheses and statistical model used in the first experiment described in Bem (2011). It will be argued that a thorough investigation of these problems in the context of more encompassing hypotheses and statistical models is called for if Bayesian psychologists want to add a well-f...
Joris Mulder - One of the best experts on this subject based on the ideXlab platform.
-
bayesian model selection of informative hypotheses for repeated measurements
Journal of Mathematical Psychology, 2009Co-Authors: Joris Mulder, Irene Klugkist, Rens Van De Schoot, Wim Meeus, Maarten H W Selfhout, Herbert HoijtinkAbstract:When analyzing repeated measurements data, researchers often have expectations about the relations between the measurement means. The expectations can often be formalized using equality and inequality constraints between (i) the measurement means over time, (ii) the measurement means between groups, (iii) the means adjusted for time-invariant covariates, and (iv) the means adjusted for time-varying covariates. The result is a set of informative hypotheses. In this paper, the Bayes factor is used to determine which hypothesis receives most support from the data. A pivotal element in the Bayesian framework is the specification of the Prior. To avoid Subjective Prior specification, training data in combination with restrictions on the measurement means are used to obtain so-called constrained posterior Priors. A simulation study and an empirical example from developmental psychology show that this Prior results in Bayes factors with desirable properties.
Andrew A Hicks - One of the best experts on this subject based on the ideXlab platform.
-
snp Prioritization using a bayesian probability of association
Genetic Epidemiology, 2013Co-Authors: John R Thompson, Martin Gogele, Christian X Weichenberger, Mirko Modenese, John Attia, Jennifer H Barrett, Michael Boehnke, Alessandro De Grandi, Francisco S Domingues, Andrew A HicksAbstract:Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, Prioritization is usually based on the p-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently Subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified fourteen important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers’ Subjective Prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on Subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the p-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP Prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with p-values alone.
