Unanswered Questions
3,298 questions with no upvoted or accepted answers
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Help me understand the Bayesian kernel density estimation (Sibisi and Skilling, 1996)
Sibisi and Skilling (1996, also mentioned in the 1997 paper) define Bayesian kernel density as
$$ f(x) = \int dx' \,\phi(x')\, K(x, x') \tag{2} $$
Here the kernel $K$ is an assigned smooth ...
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12
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517
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Official name of a common type of Bayesian simulation study
There is a kind of simulation study that is commonly used to validate an implementation of a Bayesian model:
For independent replication $i = 1, ..., n$:
Draw a set of "true" parameters ...
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Fourier transform of a Gaussian process
I would like to discuss and ask a question regarding the Fourier transform of a Gaussian process, if it makes sense.
For that purpose, let me describe the following situation.
Let $z(s)$ be a ...
CommunityBot
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11
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Pope effect on pizza - Regression with presence absence and similarity data as dependent variables
I'm trying to figure out the right way to set up a regression when the dependent variables are presence absence data (of pizzas), and the similarity between the present pizzas. Bear with the story:
...
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11
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Hypergeometric: how do I construct a credibility interval around K (population successes) in R?
I have a problem for which I believe I should use the hypergeometric distribution, but I can't figure out how to do it in R.
Say I have a bag of marbles with known number ($N$) of marbles, but the ...
CommunityBot
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10
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3
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232
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How to guess the size of a set?
Assume we have a set of unique words and draw a number $n$ of them using simple-random-sampling without replacement independently in each round. We have several rounds and try to guess the set size ...
CommunityBot
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Rationale behind Good–Turing frequency estimation?
Good–Turing frequency estimation is a smoothing estimator for estimating a multinomial distribution. It seems very convoluted.
From mathematical statistics point of view, what is the rationale
behind ...
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9
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1
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PyMC3 implementation of Bayesian MMM: poor posterior inference
Google released a whitepaper on Media Mix Modelling (MMM) in 2017; vanilla MMM (established in the 1960s) uses multivariate regression. It's a decent mechanism to understand which of your marketing ...
CommunityBot
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9
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Singular fit with simplest random structure in lmer (lme4), is a Bayesian approach the only option?
I'm running a mixed model with the lmer function from the lme4 package in R and ran into some issues with singular fits. I get the warning message 'singular fit', ...
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Implementing Predictive Posterior Distribution Using Stan
Background
I had an example that sought to demonstrate the posterior predictive distribution in the context of a normal measurement model. The data that was used is as follows:
...
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Mean, median, or mode of skewed posterior?
I'm estimating an ICC from 2 and 3-level hierarchical models using rstanarm. The simplest models are: y ~ (1|group) or ...
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Density estimation/approximation from MCMC samples
I'm looking to accurately describe the density function of a multivariate posterior probability distribution based on samples from MCMC. As far as I know, in most cases this is done either with a ...
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216
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Generalization of degrees of freedom for t distribution for coefficients after multiple imputation
Donald Rubin has shown that regression coefficient estimates have fatter tails after multiple imputation and has provided a formula for the degrees of freedom to use as a t-distribution approximation ...
- 101k
9
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3k
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Horseshoe priors and random slope/intercept regressions
I'm interested in using the horseshoe prior (or the related hierarchical-shrinkage family of priors) for regression coefficients of a traditional multilevel regression (e.g., random slopes/intercepts)....
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8
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Priors as Controls : Bayesian Regression
I have a general question about Bayesian Regression Modeling and how a prior might be used as a means to control for (close to) simultaneous events. I often face a situation where I have a time series ...
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