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Results tagged with Search options user 2310
12 results

Bayesian inference is a method of statistical inference that relies on treating the model parameters as random variables and applying Bayes' theorem to deduce subjective probability statements about the parameters or hypotheses, conditional on the observed dataset.

As a practical example: I do some Bayesian modeling using splines. A common problem with splines is knot selection. One popular possibility is to use a Reversible Jump Markov Chain Monte Carlo … only 'partially Bayesian' because for a 'fully Bayesian' approach priors would need to be placed on these coefficients (and new coefficients proposed during each iteration), but then the Least Squares estimates do not work for the RJMCMC scheme, and things become much more difficult. …
answered Jul 8 '12 by Glen
A number of papers have been written on using Bayesian methods to estimate diagnostic testing parameters (false-positive, false-negative, ...). The Bayesian method is often preferred due to the fact … problem: An Application of a Bayesian Approach in Diagnostic Testing Problems in the Absence of a Gold Standard …
answered Sep 1 '12 by Glen
I've at least glanced at most of these on this list and none are as good as the new Bayesian Ideas and Data Analysis in my opinion. Edit: It is easy to immediately begin doing Bayesian analysis … website. Covers a decent amount of theory but the focus is applications. Lots of examples over a wide range of models. Nice chapter on Bayesian Nonparametrics. Winbugs, R, and SAS examples. I …
answered Mar 13 '11 by Glen
modeled using a logistic relationship in a GLM. Since no prior information, or distributions, are used I do not consider it Bayesian. …
answered Dec 15 '13 by Glen
I'm working on a Bayesian Regression problem where I would like to estimate the beta coefficients subject to this constraint (penalty): $\sum|\beta_i|<C$ or similarly $\sum \beta_i^2<C$ Which is … basically a Lasso or L2 Penalty. Now, if I understand correctly, we constrain the coefficients through the prior in Bayesian analysis. Therefore my question is what would an appropriate prior be for …
asked Feb 16 '12 by Glen
These two links provide some R (and C) code examples of implementing a DP normal mixture: http://ice.uchicago.edu/2008_presentations/Rossi/density_estimation_with_DP_priors.ppt http://www.duke.edu/~ …
answered Dec 9 '10 by Glen
The x-axis are group assignments and the y-axis is the corresponding probability. $\alpha$ is the prior controlling how much you weigh previously selected groups when selecting a new group assignment …