Bayesian methods
Density Estimation Using Nonparametric Bayesian Methods
... non-Bayesian methods which have been used to estimate the density function, such as histogram estimates, kernel estimates, estimates using Fourier series ex- pansions and wavelet-based ...Nonparametric ...
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Bayesian Methods for Improving Credit Scoring Models
... between Bayesian methods and pooling is that the latter is only possible if full access to additional data sets is ...the Bayesian method is that it provides a natural way for dealing with structural ...
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Bayesian methods in clinical trials: a Bayesian analysis of ECOG trials E1684 and E1690
... using Bayesian methods. The Bayesian meth- odology lends itself well into this type of analysis since the E1684 data can effectively be used as prior information for the E1690 ...the Bayesian ...
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Computational Bayesian Methods for Insurance Premium Estimation
... The method exposed here assumes that one is able to simulate random variables from the distribution of interest. Often with Bayesian methods it happens that one has a function that is proportional to the ...
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Bayesian Methods for Nonlinear and Discrete Data with Complex Dependence.
... converged Bayesian algorithms from the previous section, we used GSAS-II to generate a calculated diffraction ...the Bayesian models along with the Rietveld point estimates as vertical lines with ± 2 ...
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Some Bayesian methods for univariate density estimation
... solely Bayesian methods whereas Leonard, ...for Bayesian and presented results from a maximum likelihood ...using Bayesian techniques, but conditioned on the density integrating to one rather ...
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Advances in Nonparametric Bayesian Methods for Clustering and Classification.
... Nonparametric Bayesian methods have proven to be extremely useful due to their flexibility and applicability to a wide range of ...parametric Bayesian techniques are presented for classification and ...
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Bayesian methods for empirical macroeconomics with big data
... First, Bayesian analysis using the Minnesota prior is computationally simple, involving only analytical results and with no need to use MCMC ...MCMC methods are computationally ...
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Bayesian methods for hierarchical distance sampling models
... Bayesian methods are becoming increasingly popular for modelling wildlife populations and abun- dances ...their methods for line-transect data and use the half-normal detection ...a Bayesian ...
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Bayesian Methods for Images and Trees
... (iii), Bayesian methods allow for joint inference since we draw samples from the joint posterior distributions, as demonstrated by the nu- merical results in Section ...adaptive methods are hard to ...
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Bayesian methods for system reliability and community detection
... a Bayesian nonparametric partition model for the community structure in a ...the Bayesian techniques such as MCMC algorithms, a decision theory framework for obtaining communities, model selection, and ...
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Bayesian Methods for Functional and Time Series Data
... a Bayesian Lasso prior for shrinkage in dynamic linear models, while Korobilis (2013a) consider several (non-dynamic) scale mixture priors for time series re- ...
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Bayesian methods for gravitational waves and neural networks
... The Blind Accelerated Multimodal Bayesian Inference (BAMBI) algorithm combines nested sampling and neural networks. After a specified number of new samples from M ULTI N EST have been obtained (specified by the ...
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Exploring Bayesian Methods for the Measurement of Operational Risk
... – Possibly building on existing qualitative risk assessment methods (e.g. CAMEL, RATE) where different levels of supervision apply depending on the risk rating of the firm. But it may be very difficult to maintain ...
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Bayesian Methods for High Dimensional Models in Brain Imaging.
... nonparametric Bayesian analysis, we put prior distributions on the smooth functions appearing in the graphon representation through basis expansion in tensor products of B-splines, and on the coefficients of the ...
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ESSAYS ON EMPIRICAL ASSET PRICING USING BAYESIAN METHODS
... the Bayesian approach is attractive in that it permits the researcher to obtain posterior probabilities of different models and ...a Bayesian variable selection methodology to try to find which factors ...
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Bayesian Methods for Optimal Treatment Allocation and Causal Inference.
... these methods, the policy via α and κ(α) is computed using Monte Carlo simulation given posterior samples, using the fit to the training ...these methods are approximated using sample means over 1,000,000 ...
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Advances in Bayesian Methods for High-Dimensional Environmental Data.
... The method must be tuned by selecting either the confidence level α ∈ (0, 1) or the penalty parameter λ > 0. For any given dataset, there is a one-to-one relationship between α and λ and the path of estimates obtained ...
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On Bayesian Methods in Network Regression
... and Bayesian shrinkage [109],[17],[5] ...these methods turns out to be satisfactory, their in- terpretability is limited to individual edge selection, which is scientifically less interesting than ...
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Bayesian Methods for High-dimensional Data.
... (Mallows, 1973), AIC (Akaike, 1974), and BIC (Schwarz et al., 1978) have been proposed, offering a trade-off between model complexity and goodness-of-fit of the model. When the number of covariates is larger than the ...
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