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Results tagged with bayesian
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user 1146
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.
0
votes
What to call this intermediate step in my maximum a posteriori calculation?
I don't know of an official name, but I would call it something like 'the MAP conditional on P1'.
- 4,590
2
votes
Accepted
Learning from small number of sources
Channeling Andrew Gelman:
I think you do want to use a hierarchical model. There are two hyperparameters so there's some non-identifiability, but that's the reality, and just means you'll have to ma …
- 4,590
2
votes
Study replication from a Bayesian point of view
This is not a exclusively Bayesian concept, there are many frequentist meta-analysis, but as this chapter points out, it is a good fit for Bayesian statistics. … A google search of 'bayesian meta-analysis' turns up many articles. …
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13
votes
2
answers
541
views
What should I know about designing a good Hybrid/Hamiltonian Monte Carlo algorithm?
I am designing a Hybrid Monte Carlo sampling algorithm for PyMC, and I am trying to make it as fuss free and general as possible, so I am looking for good advice on designing an HMC algorithm. I have …
24
votes
1
answer
8k
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What MCMC algorithms/techniques are used for discrete parameters?
I know a fair amount about fitting continuous parameters particularly gradient-based methods, but not much about fitting discrete parameters.
What are commonly used MCMC algorithms/techniques for fi …
- 4,590
8
votes
5
answers
770
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Papers on Bayesian factor analysis?
I am interested in fitting a factor analysis-like model on asset returns or other similar latent variable models. What are good papers to read on this topic? I am particularly interested in how to han …
- 4,590
11
votes
3
answers
2k
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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 …
- 4,590
4
votes
0
answers
122
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Practical problems with difficult posteriors
I'm looking for difficult Bayesian inference problems to test out different Monte Carlo sampling methods. …
- 4,590
8
votes
1
answer
540
views
Estimating a sparse inverse covariance matrix with known sparsity
If the distribution in question is the posterior of a Bayesian graphical model, many or most of the variables will be conditionally independent of each other. …
- 4,590
7
votes
4
answers
1k
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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 hav …
- 4,590
7
votes
Accepted
What's a good prior distribution for degrees of freedom in a t distribution?
On page 372 of ARM, Gelman and Hill mention using a uniform distribution on the inverse of DF between 1/DF = .5 and 1/DF = 0.
Specifically, in BUGS, they use:
nu.y <- 1/nu.inv.y
nu.inv.y ~ dunif( …
- 4,590
6
votes
Accepted
What is the Bayesian counterpart to a two-sample t-test with unequal variances?
While you can do this in a Bayesian way, have you considered whether it would actually be better to estimate the difference in the means rather than test whether they are different? …
- 4,590
8
votes
Accepted
What methods can be used to specify priors from data?
If you have all this data, I think the best answer is to actually fit a single large model, using Hierarchical Modeling rather than do it in two steps (generating a prior then fitting a model). This i …
- 4,590
1
vote
Tips and tricks to get started with statistical modeling?
The best introductory Bayesian book I have found is Data Analysis - A Bayesian Tutorial. It is quite practical. …
5
votes
Accepted
Estimating distribution parameters from few data points
I believe that Gelman's books (Bayesian Data Analysis and Data Analysis Using Regression and Multilevel/Hierarchical Models) talk quite a bit about this kind of thing (though perhaps not a lot for variance …
- 4,590