All Questions
787
questions
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13
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How to choose default uninformative prior in the R Package BAS
I'm conducting a Bayesian multilevel logistic regression based on the Rpackage BAS. I'm a beginner in Bayesian statistics.
But in bas.glm, I don't understand and I don't know how to specify my prior. ...
- 1
1
vote
0
answers
27
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how can predictive distributions be considered as expectations?
I guess that the prior and posterior predictive distributions can be considered expectation of $p(y|\theta )$ (in case of prior predictive distribution) and $p(\widetilde{y}|\theta )$ (in case of ...
0
votes
0
answers
26
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How to obtain likelihood ($P(B/R)$ given the prior $P(R)$ and the posterior $P(R/B)$
I am working on a topic related to multiple-choice response. I would like to measure the efficiency of the information source (or a student’s information search) and I believe Bayesian statistics is ...
- 21
3
votes
1
answer
41
views
Does the example given correspond to a prior predictive check?
Could someone explain to me precisely what is meant by prior predictive check, in Bayesian inference? In some documents, one uses observed data (“in which we ...
- 189
1
vote
1
answer
39
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How to decide the parameters of a Gamma distribution for a Gamma-Poisson model?
In Bayesian inference, the Gamma-Poisson model uses usually a Gamma($\alpha$,$\beta$) prior on the $\lambda$ parameter of the Poisson distribution.
Are there any rules for setting appropriate values ...
- 189
0
votes
0
answers
17
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Strange Variance Term for Normal Prior $w^2\sigma^2$
I've attached two screenshots, one with the question and one with the answer. It seems to me that the prior is wrong and it should include $w^2$ not $w^2\sigma^2$
I apologise for, including such a ...
- 161
1
vote
1
answer
44
views
BIC with non-negligible priors
I want to do model selection based on the best-fit/MAP/marginal posterior I find from an MCMC and likelihood maximization. I have a likelihood $\mathcal{L}(X|\theta)$, some informative priors $\pi(\...
- 13
0
votes
1
answer
72
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Avoid singular fits in mixed models in R with blme - checking layman's priors
While fitting linear mixed models, I would like to avoid zero random-effects (ranef(model)) and cluster-level SD estimates (...
- 43
0
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0
answers
13
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Turning a list of cost into categorical probability mass distribution
Background
Given a noisy dataset $D$, I have to solve a classification problem where the possible anserwer is $i\in\{1,\dots,N\}$. So far I can get pretty decent result with an algorithm that, based ...
- 373
0
votes
0
answers
19
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How should uncertainties be treated when scaling data for optimisation
I have a large dataset for which I am using Bayesian statistics for parameter estimation and model selection (using MultiNest for more detail).
This involves setting a prior over which the nested ...
- 33
9
votes
2
answers
426
views
How is data generated when using an improper prior
Let $X$ be an $\mathcal{X}$ valued random variable. We are doing Bayesian statistics. Suppose that $\theta$ is a $\Theta$ valued random variable with known prior distribution $\Pi$ and that the ...
- 456
0
votes
0
answers
23
views
Random sequence generator algorithm non informative piror distribution
I want to conduct a Bayesian statistical analysis of a sequence generation phenomenon.
The sequences generated contain elements from a known alphabet.
Working on that, I have tried to define the prior ...
1
vote
0
answers
32
views
How to interpret a noninformative joint prior?
I am currently working on a homework assignment and have the following question:
$\theta_1$ and $\theta_2$ are parameters of interest and $y_1$ and $y_2$ are the likelihood functions which are $\text{...
- 11
1
vote
0
answers
35
views
How to derive conditional destribution of MVN variable
I am working with following model specifications (Regression_ Modelle, Methoden und Anwendungen-Springer-Verlag Berlin Heidelberg (2009), p. 147):
$$Y \sim MVN(X\beta, \sigma^2I)$$
$$\beta|\sigma^2 \...
- 137
1
vote
1
answer
66
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Full conditional posteriors
so up to now I dealt with posteriors in the form of:
$$p(\theta|x) \propto p(x|\theta) p(\theta)$$
No we started to model a linear regression with the bayesian approach:
$$Y \sim MVN(X\beta, \sigma^2I)...
- 137