# Tagged Questions

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### A Question from textbook “Learning with Kernels”

I am reading the book "Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)". I finished the first chapter and didn't ...
216 views

### Q: what book on Bayesian statistics, preferably with R?

I am frequentist by training and practice, but I'd like to learn more about Bayesian statistics. I know the basics, but I would be at a loss if I had to, for example, replace my normal ANOVA ...
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### Good libraries for working with probabilistic graphical models?

Could someone recommend some well-maintained and up-to-date libraries for working with probabilistic graphical models? I noticed that there are some libraries for R listed here and one for C++, but ...
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### References for Bayesian group-Lasso for probit/logit regression

Does anyone have a paper or other references on Bayesian group-Lasso for probit/logit model or GLM (generalized linear models) in general? I could not find any paper that explicitly deals with this.
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### The meaning of convergence in Variational Inference?

My friend and I are discussing about the convergence of Variational Inference, especial for Expectation Propagation method. After running some loops, the likelihood of my graphical model can be ...
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### Jeffreys prior for linear regression model

Consider the linear regression model $${\bf y} = {\bf X}\beta + {\bf e},$$ where ${\bf y}$ is an $n\times 1$ vector, $\beta$ is a $p\times 1$ vector, ${\bf e}$ is an $n\times 1$ vector. Assume also ...
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### Empirical Bayes/MCMC references

I'm interested in references for running empirical Bayes (EB) in conjunction with MCMC. The closest thing I've found to what I'm looking at is a surprisingly recent and somewhat obscure paper ...
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### Non-informative prior for integer restricted parameters

Does anyone have advice/references on choosing a minimally informative prior for the shape parameter of an Erlang distribution (gamma distribution with shape parameter restricted to integer space), or ...
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### Random factor nested within fixed factor under Bayesian approach

I am working on my posgraduate thesis using horse competition data to estimate genetic parameters of performance, basically time and placing traits. By means of a Bayesian approach I would like to ...
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### Examples of marginalization on non-trivial Bayesian networks

What is a good resource for finding out how to code integration (marginalization) on a non-trivial Bayesian network? I am interested in this equation in particular:  p(r_{i+1},~v_{i+1},~k | y_{\leq ...
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### Statistics wording question

I am reading a statistical procedure trying to figure out and understand what's going on. The statement says "Compute the posterior on $\mu$." Does this mean compute $p(\mu)$? Does this mean ...
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### Has anyone studied the properties of the following method of estimation?

In Bayesian inference, we usually sample from the posterior $f(\theta_1,\theta_2|-)$ via MCMC to compute point estimates for the parameters of interest. I am investigating an alternate form of ...
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### Looking for step by step example of sampling from DAG in Bayesian model

I am looking for a tutorial type example that shows the step by step process sampling from a simple hierarchical model. For example, I am trying to study the distribution of ...
692 views

### Next steps after “Bayesian Reasoning and Machine Learning”

I'm currently going through "Bayesian Reasoning and Machine Learning" by David Barber and it is an extremely well written and engaging book for learning the fundamentals. So a question to someone who ...
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### Kolmogorov's paper defining Bayesian sufficiency

I'm looking for a translation to either English, French or German of Kolmogorov's Russian paper Kolmogorov, A. (1942). Sur l’estimation statistique des paramètres de la loi de Gauss. Bull. Acad. Sci. ...
191 views

### Introductory textbook on nonparametric Bayesian models?

I'd like to wrap my head around this topic but learning from white-papers and tutorials is hard because there are many gaps which are usually filled in textbooks. If it is important I have ...
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### How to present the Bayesian approach

I am writing a dissertation in which I use maximum likelihood estimation and an alternative Bayesian approach. I have written up the maximum likelihood estimation approach. However, I need some advice ...
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### Free PDF for Bayes

Is there an good book/pdf similar to Elements of Statistical Learning that's available for free, online that deals with Bayesian statistics, ideally with code for ...
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### I'm looking for solution manual “A first course in Bayesian statistical methods”

Im looking for a solution manual for Peter Hoff's A first course in Bayesian statistical methods. I cannot find it online, does anybody know whether there is a manual available? Alternatively does ...
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### Reference on examples with R codes for Bayesian simulation based methods of posterior approximation

I have been trying to learn the Bayesian simulation based methods of posterior approximation. Although the theories are now quite clear but I am seeking for some examples with R codes so that I can ...
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### Any reference book for the axiom of indifference or insufficient reason

I need a book as a reference for the "principle of indifference" or "principle of insufficient reason". Any suggestion?
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### How is this equation read?

I want to understand this paper on brain tumour segmentation. How is this equation read? I'm guessing $q_i(t_i)$ represents the likelihood of tumour on voxel i.Is q usually used to represent ...
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### Estimating a sparse inverse covariance matrix with known sparsity

The inverse of the covariance matrix for a distribution can be a good value for the mass matrix of a Hamiltonian monte carlo distribution. If the distribution in question is the posterior of a ...
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### Elementary statistics for jurors

I have been summoned for jury duty. I am conscious of the relevance of statistics to some jury trials. For example, the concept of "base rate" and its application to probability calculations is ...
319 views

### Textbook deriving Metropolis-Hastings and Gibbs Sampling

I have fairly good practical experience with Metropolis-Hastings and Gibbs sampling, but I want to get a better mathematical understanding of these algorithms. What are some good textbooks or articles ...
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### Do Bayesians ever argue there are cases in which their approach generalizes/overlaps with the frequentist approach?

Do Bayesians ever argue that their approach generalizes the frequentist approach, because one can use non-informative priors and therefore, can recover a typical frequentist model structure? Can ...
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### Cross-validation vs empirical Bayes for estimating hyperparameters

Given a hierarchical model $p(x|\phi,\theta)$, I want a two stage process to fit the model. First, fix a handful of hyperparameters $\theta$, and then do Bayesian inference on the rest of the ...
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### Canonical reference for Naive Bayes classifier

I'm looking for a reference for the Naive bayse classifier to put in my work. Not sure what I'm missing but a scholar search didn't yield any meaningful results. any idea ? update: I meant for a ...
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### A good book with equal stress on theory and math

I have had enough courses on statistics during my school years and at the university. I have a fair understanding of the concepts, such as, CI, p-values, interpreting statistical significance, ...
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### What are good techniques and resources for teaching Bayes theorem?

My friend and I want to do a hands on tutorial on Bayes theorem for the Seattle LessWrong group. Neither of us have done this before, so we're searching for prior art; techniques that other people ...
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### Resources about Gibbs sampling in hybrid Bayesian networks

Greetings, I've been trying to get my hands on a substantial resource for using Gibbs sampling in hybrid Bayesian networks, that is, networks with both continuous and discrete variables. So far I ...
3k views

### Bayesian statistics tutorial

I am trying to get upto speed in Bayesian Statistics. I have a little bit of stats background (STAT 101) but not too much - I think I can understand prior, posterior, and likelihood :D. I don't want ...
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### Tips and tricks to get started with statistical modeling?

I work in the field of data mining and have had very little formal schooling in statistics. Lately I have been reading a lot of work that focuses on Bayesian paradigms for learning and mining, which ...