All Questions
24
questions
3
votes
1
answer
111
views
Choosing Bayesian Priors [duplicate]
I am fairly new to Bayesian Modeling, however I am experimenting with such framework in order to produce several estimates.
The part I am struggling the most with is the selection of prior ...
1
vote
0
answers
48
views
Power of Bernoulli likelihood in Jags (R2jags) [closed]
In a fixed power prior model, the model is set up as:
$$ \pi(p_i \mid \alpha,\mathcal{D}_0) \propto L(p_i\mid \mathcal{D}_0)^{w} \pi(p_i) $$
Suppose that the event follows a Bernoulli distribution ...
- 21
1
vote
1
answer
139
views
Using a Generalized Beta Distribution of the Second Kind as a Prior in Stan Linear Regression
So I'm considering a simple linear regression model with $p = 1$ predictor $$y = \beta x + \epsilon$$ where $\epsilon \sim N(0,\sigma^2)$. I want to use a generalised beta distribution of the second ...
- 331
1
vote
1
answer
226
views
In JAGS, how can I fix a parameter to a distribution, as opposed to just a constant?
The first code chunk below (model1) is a JAGS script designed to estimate a two-group Gaussian mixture model with unequal variances. I am looking for a way to fix one of the parameters (say $\mu_2$) ...
- 2,856
3
votes
1
answer
1k
views
Default Priors for Intercept and Standard Deviations in R package brms
The only resource I found explaining the default priors in brms is its manual (newest version, updated 03/14/2021) for function set_prior().
For the intercept, the manual does not specify how the ...
- 31
1
vote
0
answers
436
views
Is my Stan model correct? The Jeffreys prior for a heteroscedastic mixed-effects model
I am using rstan to obtain MCMC samples from a heteroscedastic mixed-effects model with different residual variances $\sigma_j^2$ for each experimental condition $j$.
One assumption is the Jeffreys ...
1
vote
1
answer
388
views
Reproduce results of bayesglm with stan_glm
As indicated in the title, I am trying to reproduce the
results of the bayesglm function with the stan_glm. In
principle, the ...
- 63
3
votes
1
answer
492
views
How to get around to "Argument 'coef' may not be specified when using boundaries."?
I have a model, the brms code is given below. It is a system of equations (I am estimating demand for two categories of goods). Economic theory tells me that the ...
0
votes
0
answers
118
views
How do I implement a default prior of cauchy(0,1) in rstanarm?
What I intend to do is use a default prior on my coefficients, and then to compute Bayes Factors for those coefficients.
Rouder and Morey (2012) say: "When using the Cauchy prior, s describes the ...
- 2,651
1
vote
0
answers
628
views
Choosing a prior for the intercept in a logistic regression with increased -INF probability?
I am trying to fit a simple logistic regression of the kind:
n ~ binomial(N, theta)
theta = inv_logit( a + x * b )
where x is either 0 or 1 depending if a ...
- 111
0
votes
2
answers
687
views
Pick a prior for my bayesian generalised linear model with binary outcomes
I need help in my choice of a prior for a bayesian model.
I have data from a set of participants responding to a set of yes/no questions. Answers are correct or incorrect. I suspect some questions ...
- 137
5
votes
1
answer
328
views
Why in Hamiltonian MCMC do we multiply the posterior distribution by the likelihood?
So maybe I am misunderstanding what the author is staying, but I am reading Chapter 14 of Kruschke's Doing Bayesian Analysis. I am reading about the software Stan and how it uses the Hamiltonian MCMC ...
- 3,273
5
votes
1
answer
619
views
Why does Stan initialize an MCMC chain with a random value generated uniformly from [-2, 2] instead of a random value generated from the prior?
From Stan reference,
The default is to randomly generate initial values between -2 and 2 on
the unconstrained support
It seems to me that it makes more sense to randomly generate initial values ...
- 8,184
11
votes
1
answer
619
views
What do blank cells mean in the output of prior_summary in the brms package?
The brms package is an R package for fitting Bayesian models using lme4-like syntax using Stan as the back-end. In the package, ...
- 749
9
votes
0
answers
3k
views
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:
...
- 2,096