Likelihood and Bayesian Score-based Methods
Maximum Likelihood Estimation Using Bayesian Monte Carlo Methods
... maximum likelihood estimation via Bayesian Monte Carlo ...maximum likelihood estimation problems: simple linear regression, multiple linear regression, a stochastic dynamical model (Gompertz), and a ...
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Empirical likelihood-based adjustment methods
... 227 patients from 12 ambulatory care settings, out of whom 218 provided data for the first treatment period. The most important eligibility criteria included being more than 40 years old, having a Kellgren-Lawrence ...
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Bayesian synthetic likelihood
... pseudo-marginal methods as these values are simply ...the likelihood being grossly overestimated and the MCMC chain becoming stuck at that value for a long ...
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Bayesian synthetic likelihood
... pseudo-marginal methods (Doucet et ...pseudo-marginal methods generally. The odd underestimated log-likelihood does not cause much problem in pseudo-marginal methods as these values are simply ...
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Soft-margin SVMs in the HDLSS context (Maximum Likelihood and Bayesian Methods)
... Aoshima and Yata [4] considered quadratic classifiers in general and discussed asymptotic properties and optimality of the classifiers under high‐dimension, non‐sparse settings.. linear [r] ...
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Context dependent score based Bayesian information criteria
... posterior score based on a plug in or posterior expected value is problematic in that it uses the data twice in estimation and ...new Bayesian posterior score information criterion (BPSIC), ...
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The Bayesian Score Statistic
... region based statistics, see among others Poirier ...region based statistics do not require the specification of prior probabilities and proper prior densities for the parameters in the competing ...
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The Bayesian Score Statistic
... is based on exact finite sample arguments while the results of Nicolaou (1993) and Tibshirani (1989) are obtained in an asymptotic setting but their results therefore hold for a larger class of models and ...
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Classic and Bayesian Tree-Based Methods
... Tree-based methods are nonparametric techniques and machine-learning meth- ods for data prediction and exploratory ...mining methods and can be used for predicting different types of outcome ...
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Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions
... computing Likelihood Ratios (LR) from multimodal score distributions produced by an Automated Fingerprint Identification System (AFIS) feature extraction and comparison ...similarity score (e.g. one ...
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Auxiliary likelihood based approximate Bayesian computation in state space models
... ABC methods in SSMs for which exact methods are essentially ...auxiliary likelihood approach to ABC in the SSM context is to define, via (7) and (8), a sensible parsimonious approximation to the true ...
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Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
... The right column in Figure 12 compares our model-based results with the solution by Numminen et al. (2013) (blue horizontal lines with triangles) and with results by the population Monte Carlo (PMC) ABC algorithm ...
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Bayesian empirical likelihood for quantile regression
... each Bayesian method for τ = ...MCMC based method, because it uses the true parametric likelihood under the model, which is generally unknown in ...working likelihood is the objective function ...
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Likelihood: Frequentist vs Bayesian Reasoning
... the methods do a good job of estimating α ; and they all do a pretty poor job with only a little ...maximum likelihood estimate of α is much closer to the actual value of α than the Bayesian estimate ...
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How likelihood and identification went Bayesian
... In the paradigm cases of the identi¯cation problem in the regression and simultaneous equations models all the points in the sample space are noninformative if any is{the same is true for Neath and Samaniego's binomial ...
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A Predictive Likelihood Approach to Bayesian Averaging
... scheme based on the trace of the MSE matrix, the model rank, two approaches to the predictive likelihood compute and equal ...schemes based on predictive likelihood assign almost zero weights ...
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Discriminative likelihood score weighting based on acoustic phonetic classification for speaker identification
... or SVM [2,6,10]. Although SVM has shown to be very effective in two-class classification problems such as speaker verification, it may need further algorithmic development in the multi-class tasks including speaker ...
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A Comparison between Maximum Likelihood and Bayesian Estimation Methods for a Shape Parameter of the Weibull-Exponential Distribution
... the Bayesian analysis of a shape parameter of the Weibull-Exponential distribution in this ...maximum likelihood under a comprehensive simulation ...
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Methods of likelihood based inference for constructing stochastic climate models
... In Section 7.2 we considered five different algorithms to sample the Stability Ma- trix. These included basic rejection and random walk sampling, which were found to be inefficient compared to a component-wise algorithm. ...
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Inference and Application of Likelihood Based Methods for Hidden Markov Models
... sufficient. Based on a quasi-likelihood which neglects the dependence structure of the regime, our tests extend existing tests for independent finite ...
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