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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.

3
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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
3
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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
9
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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
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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
5
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4answers
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
8
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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
8
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3answers
I am interested in fitting a Bayesian Two Factor ANOVA in BUGS or by utilizing some R package. Unfortunately I am having a hard time finding resources on this topic. Any suggestions? Even an article describing the approach would be helpful. …
asked Jun 1 '11 by Glen
9
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One method to deal with this is to increment all counts by 1. This is known as Laplace smoothing. If you Google Laplace smoothing and Naive Bayes you will find many references.
answered Sep 2 '11 by Glen
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You don't have a reproducible example but I'm pretty sure I know what is going on. If any of your observations are outside a, b then the likelihood is undefined and you get the error (which must be h …
answered Jan 23 '18 by Glen
1
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In the output statement in proc logistic you can request cross-validated predictions (I believe they are leave one out predictions). There is also an option to produce an ROC curve. If you need to g …
answered Apr 26 '12 by Glen
1
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4answers
If one wanted to use Kernel Regression in a Bayesian Framework, any ideas on how one would go about it? Kernel Regression …
asked Jun 21 '11 by Glen
3
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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 …
answered Apr 29 '14 by Glen