Bayes Factors
Novel Bayes Factors That Capture Expert Uncertainty in Prior Density Specification in Genetic Association Studies.
... The R code given below will calculate a vector of approximate Bayes factors for a set of SNPs which have been genotyped and analysed using single SNP logistic regression models. These models should all ...
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A Note on the Connection between Likelihood Inference, Bayes Factors, and P Values
... setting a specific alternative hypothesis. This idea is not new, nor is it without merit. Fisher did not consider the specification of an alternative to be an impor- tant aspect of a testing problem. The feud between ...
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Improving Inferences about Null Effects with Bayes Factors and Equivalence Tests
... and Bayes factors (based on Bayesian ...interpret Bayes factors and equivalence ...between Bayes factors and equivalence tests are discussed, and we also note when and why they ...
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Novel Bayes Factors That Capture Expert Uncertainty in Prior Density Specification in Genetic Association Studies.
... ABSTRACT: Bayes factors (BFs) are becoming increasingly important tools in genetic association studies, partly because they provide a natural framework for including prior ...
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Bayes factors vs P values
... the Bayes factor is not probability itself but a ratio of probabilities, ranging from zero to ...a Bayes factor of ...whereas Bayes factors represent the relative probability assigned to the ...
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Bayes factors for superiority, non-inferiority, and equivalence designs
... distributions are truncated at zero. Because both dis- tributions are normalized to have a density of 1, the effect of this truncation is especially strong for a dis- tribution that falls almost entirely inside the ...
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Optional stopping with Bayes factors: A categorization and extension of folklore results, with an application to invariant situations
... the Bayes factor method; we do not make any claims about other types of Bayesian ...the Bayes factor and the posterior odds usually have a distribution with full support, with variable stopping times, the ...
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Measuring the effect of observations using the posterior and intrinsic Bayes factors with vague prior information
... The main objective of this paper is to develop diagnostic measures for model checking and also model selection using the posterior and the intrinsic Bayes factor when the prior informati[r] ...
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Novel Bayes Factors That Capture Expert Uncertainty in Prior Density Specification in Genetic Association Studies.
... We have developed several new forms of approximate Bayes factor. These include three parametric families and one fixed form, each relating to a different prior distribution on W , where we assume that the prior ...
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Testing for cointegration rank using Bayes factors
... and van Dijk 1994, who proposed using a Jerey's prior instead of diuse prior for the cointegrating vectors since the marginal posteriors may be nonintegrable with reduced rank of cointeg[r] ...
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A Decision Rule for Quantitative Trait Locus Detection Under the Extended Bayesian LASSO Model
... ABSTRACT Bayesian shrinkage analysis is arguably the state-of-the-art technique for large-scale multiple quantitative trait locus (QTL) mapping. However, when the shrinkage model does not involve indicator variables for ...
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Bayesian analysis of cointegrated vector autoregressive models
... In this chapter we deal with testing for multiple structural breaks in a vector error correction model as a problem of model selection and approximate the Bayes factors by Schwarz's Baye[r] ...
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JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs
... Fig. 8 Ranking of hypotheses for Reply Higgs Network. a, b Ranking of hypotheses based on Bayes factors when compared to the uniform hypothesis using multiplexes for the local and global models ...
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Abandon statistical significance
... employing Bayes factors along with conventional classifications for evaluating the strength of evidence suffer from the same or similar issues as the current use of p-values with the ...subordinate ...
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Dynamic staged trees for discrete multivariate time series : forecasting, model selection and causal analysis
... tree at time t and S is the underlying model. The most common way to achieve this is to use a conventional state space formulation. Unfortunately, this approach immediately requires the inference to be undertaken with ...
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Population sensitivity of acute flaccid paralysis and environmental surveillance for serotype 1 poliovirus in Pakistan: an observational study
... analysis. Bayes factors (BF) were used to assess the evidence in favour of each model when compared to the simple model, and may be interpreted in a similar manner to a likelihood ratio, where a BF above ...
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Incorporating unobserved heterogeneity in Weibull survival models : a Bayesian approach
... Both analysed datasets provide strong evidence for unobserved heterogene- ity, shown not to be a consequence of a small number of specific outliers. Mix- ture models are supported by the data in terms of Bayes ...
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Bayesian modelling of skewness and kurtosis with two piece scale and shape transformations
... We introduce the family of univariate double two–piece distributions, obtained by using a density– based transformation of unimodal symmetric continuous distributions with a shape parameter. The resulting distributions ...
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The capacity and resolution of spatial working memory and its role in the storage of non spatial features
... calculate Bayes factors for interactions between the factors Task and WM ...Load. Bayes factors denote the relative evidence for the null hypothesis as compared to the alternative ...
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Model based clustering of non Gaussian panel data
... The long-run equilibrium levels associated with each cluster are often quantities that we possess some prior information about. If so, it may be desirable to introduce that information through an informative prior, ...
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