Bayesian model
Bayesian model selection of structural explanatory models: Application to road accident data
... posterior model probability, p(M=k|Y=y). This procedure is referred as Bayesian Model Averaging and is implemented by using Bayes ...best model will have the highest probability. ...
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Prediction of Federal Funds Target Rate: a dynamic logistic Bayesian Model averaging approach
... the Bayesian Model Averaging methodology is based on the fact that this framework is firmly grounded on statistical theory following the rules of ...other model selection techniques, such as the ...
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Criteria for Bayesian model choice with application to variable selection
... objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior ...objective model selection priors by consider- ing their application to the problem of ...
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Robust Tracking in Aerial Imagery Based on an Ego-Motion Bayesian Model
... parametric model. In [18], a perspective camera model is computed using an optical flow algorithm for the detection of moving objects in an application of aerial visual ...camera model, which is ...
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Multi-facet determination for clustering with Bayesian networks
... A model-based clustering method called CAMI was presented by [15], which simultaneously uncovers a pair of clusterings that maximizes the data likelihood and minimizes the mutual information between ...
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Bayesian analysis for a fractional population growth model
... For multivariate distributions, we can apply the single variable slice sampling for each 𝜃 𝑖 , 𝑖 = 1, . . . , 𝑝, in turn. An example of JAGS implementation is given in Appendix. 3.2. Bayesian Model ...
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Bayesian analysis of textual data
... Instead, model based clustering assumes that observations come from a population with several subpopulations, and one models the overall population through a finite mixture of the subpopulation ...models. ...
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BAYESIAN ESTIMATION OF A SUBJECT-SPECIFIC MODEL OF VOICE PRODUCTION FOR THE CLINICAL ASSESSMENT OF VOCAL FUNCTION
... enhanced model to the devel- opment and application of a stochastic estimation method for its ...the model was fixed in the Bayesian estimation ...optimal model complexity, based upon the ...
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Learning Tractable Bayesian Networks
... practice. Bayesian networks (BNs) address this problem applying probability theory, that is long estab- lished, using probabilities to indicate different degrees of ...the model and the arcs represent the ...
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Bayesian statistics in genetics
... cases, Bayesian methods can address the question of interest more directly than a classical ...A Bayesian approach can reflect a more relevant question, which might be ‘are departures from HWE large enough ...
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A semiparametric Bayesian joint model for multiple mixed type outcomes: an application to acute myocardial infarction
... reversible jump methodology, which may not be easy to implement, even when fixing a maximal number of components. The computational cost of the resulting model is comparable to that of the nonparametric one, the ...
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Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady state signalling simulations
... To our knowledge, no single-cell data on bioleaching bacteria are currently available with such an accurate description of the underlying molecular interactions as in reference [15]. We therefore used this well-described ...
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A Bayesian strategy to enhance the performance of indoor localization systems
... These indoor location systems are usually providing a horizontal functionality enabling different types of vertical services. These services may have different needs for location accuracy and granularity [3], then being ...
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Decision boundary for discrete Bayesian network classifiers
... In this paper we have shown how to build polynomial threshold functions related to Bayesian network classifiers. Our results reveal connections between the algebraic structure of the decision functions induced by ...
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Bayesian analysis of polarization measurements
... In Bayesian Decision and Estimation Theory determining the “best” estimate of the model parameters requires defining a loss function and a decision rule (there are many good standard texts offering much more ...
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Pronóstico de grandes sismos mediante el análisis de semiperiodicidad de procesos puntuales etiquetadosForecast of large earthquakes through semi-periodicity analysis of labeled point processes
... But, beyond this simplistic model of stress accumulation and release, earthquakes can be seen to be the result of a critically self-‐organized process (e. g. Bak et al., 1988; Bak et al., 1994; Ito and Matsuzaki, ...
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Selecting “the best” nonstationary Generalized Extreme Value (GEV) distribution: on the influence of different numbers of GEV-models
... 2011). GEV is a three- parameter function in which location (μ), scale (σ) and shape (ξ) parameters define, respectively, the position of the function in respect to the origin, spread of the distribution and its tail ...
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Development of a model with Bayesian network for Data Driven Materials
... are becoming more powerful day by day. Also we have to take into account the birth of a new field in science, called data science. Data-driven methods are being used in several fields on our daily lives, from here comes ...
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A Bayesian semiparametric partially PH model for clustered time to event data
... robust model for ...mixture model induced by a completely random measure (CRM), in the class of generalized- gamma CRMs (Dykstra and Laud, 1981, Lo and Weng, 1989, Nieto-Barajas and Walker, 2004, James, ...
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A. J. Drummond, A. Rambaut, B. Shapiro, and O. G. Pybus - Bayesian skyline plots
... The Bayesian skyline plot model uses standard Markov chain Monte Carlo (MCMC) sampling procedures to estimate a posterior distribution of effective population size through time directly from a sample of ...
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