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Multiple continental radiations and correlates of diversification in Lupinus (Leguminosae): testing for key innovation with incomplete taxon sampling. Syst. Biol
"... Abstract.—Replicate radiations provide powerful comparative systems to address questions about the interplay between opportunity and innovation in driving episodes of diversification and the factors limiting their subsequent progression. However, such systems have been rarely documented at intercont ..."
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Abstract.—Replicate radiations provide powerful comparative systems to address questions about the interplay between opportunity and innovation in driving episodes of diversification and the factors limiting their subsequent progression. However, such systems have been rarely documented at intercontinental scales. Here, we evaluate the hypothesis of multiple radiations in the genus Lupinus (Leguminosae), which exhibits some of the highest known rates of net diversification in plants. Given that incomplete taxon sampling, background extinction, and lineagespecific variation in diversification rates can confound macroevolutionary inferences regarding the timing and mechanisms of cladogenesis, we used Bayesian relaxed clock phylogenetic analyses as well as MEDUSA and BiSSE birth–death likelihood models of diversification, to
Learning hybrid Bayesian networks from data
, 1998
"... We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first method ..."
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We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first methodology uses discretized data to learn the Bayesian network structure, and the original nondiscretized data for the parameterization of the learned structure. The second methodology uses nondiscretized data both to learn the Bayesian network structure and its parameterization. For the direct handling of continuous data, we propose the use of artificial neural networks as probability estimators, to be used as an integral part of the scoring metric defined to search the space of Bayesian network structures. With both methodologies, we assume the availability of a complete dataset, with no missing values or hidden variables. We report experimental results aimed at comparing the two methodologies. These results provide evidence that learning with discretized data presents advantages both in terms of efficiency and in terms of accuracy of the learned models over the alternative approach of using nondiscretized data.
Bayesian Estimation and Model Choice in Item Response Models
, 1999
"... Item response models are essential tools for analyzing results from many placement tests. Such models are used to quantify the probability of correct response as a function of unobserved examinee ability and other parameters explaining the difficulty and the discriminatory power of the questions in ..."
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Cited by 11 (1 self)
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Item response models are essential tools for analyzing results from many placement tests. Such models are used to quantify the probability of correct response as a function of unobserved examinee ability and other parameters explaining the difficulty and the discriminatory power of the questions in the test. Some of these models also incorporate a threshold parameter for the probability of the correct response to eliminate the effect of guessing the correct answer in multiple choice type tests. In this article we consider fitting of these models using the Gibbs sampler. A data augmentation method to analyze a normalogive model incorporating a threshold guessing parameter is introduced and compared with a MetropolisHastings sampling method. The proposed method is an order of magnitude better than the existing method. Another objective of this paper is to develop Bayesian model choice techniques for model discrimination. A predictive approach based on a variant of the Bayes factor is ...
Delivery: An OpenSource ModelBased Bayesian Seismic Inversion Program
, 2003
"... We introduce a new opensource toolkit for modelbased Bayesian seismic inversion called Delivery. The prior model in Delivery is a tracelocal layer stack, with rock physics information taken from log analysis and layer times initialised from picks. We allow for uncertainty in both the fluid ty ..."
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Cited by 9 (3 self)
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We introduce a new opensource toolkit for modelbased Bayesian seismic inversion called Delivery. The prior model in Delivery is a tracelocal layer stack, with rock physics information taken from log analysis and layer times initialised from picks. We allow for uncertainty in both the fluid type and saturation in reservoir layers: variation in seismic responses due to fluid e#ects are taken into account via Gassman's equation. Multiple stacks are supported, so the software implicitly performs a full AVO inversion using approximate Zoeppritz equations. The likelihood function is formed from a convolutional model with specified wavelet(s) and noise level(s). Uncertainties and irresolvabilities in the inverted models are captured by the generation of multiple stochastic models from the Bayesian posterior, all of which acceptably match the seismic data, log data, and rough initial picks of the horizons. Postinversion analysis of the inverted stochastic models then facilitates the answering of commercially useful questions, e.g. the probability of hydrocarbons, the expected reservoir volume and its uncertainty, and the distribution of net sand. Delivery is written in java, and thus platform independent, but the SU data backbone makes the inversion particularly suited to Unix/Linux environments and cluster systems.
An evaluation of a Markov chain Monte Carlo method for the Rasch model
 Applied Psychological Measurement
, 2001
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Bayesian finite mixtures: a note on prior specification and posterior computation
, 2005
"... A new method for the computation of the posterior distribution of the number k of components in a finite mixture is presented. Two aspects of prior specification are also studied: an argument is made for the use of a P oi(1) distribution as the prior for k; and methods are given for the selection of ..."
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A new method for the computation of the posterior distribution of the number k of components in a finite mixture is presented. Two aspects of prior specification are also studied: an argument is made for the use of a P oi(1) distribution as the prior for k; and methods are given for the selection of hyperparameter values in the mixture of normals model, with natural conjugate priors on the components parameters.
Bridge Estimation of the Probability Density At a Point
 Statistica Sinica
, 2000
"... Bridge estimation, as described by Meng and Wong in 1996, is used to estimate the value taken by a probability density at a point in the state space. When the normalisation of the prior density is known, this value may be used to estimate a Bayes factor. It is shown that the multiblock MetropolisH ..."
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Bridge estimation, as described by Meng and Wong in 1996, is used to estimate the value taken by a probability density at a point in the state space. When the normalisation of the prior density is known, this value may be used to estimate a Bayes factor. It is shown that the multiblock MetropolisHastings estimators of Chib and Jeliazkov (2001) are bridge estimators. This identification leads to more efficient estimators for the quantity of interest.
Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging TimeSeries Data
, 2011
"... One of the major objectives of fMRI (functional magnetic resonance imaging) studies is to determine subjectspecific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate ..."
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Cited by 7 (2 self)
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One of the major objectives of fMRI (functional magnetic resonance imaging) studies is to determine subjectspecific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate a Bayesian approach that incorporates spatial dependence in the image and allows for the taskrelated change in the BOLD signal to change dynamically over the scanning session. In this way, our model accounts for potential learning effects, in addition to other mechanisms of temporal drift in taskrelated signals. However, using the posterior for inference requires Markov chain Monte Carlo (MCMC) methods. We study the properties of the model and the MCMC algorithms through their performance on simulated and real data sets. 1