Questions tagged [jags]
"JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS." (http://mcmc-jags.sourceforge.net/)
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Conditional structure with 4 conditionals in JAGS
I'm using JAGS for Bayesian estimation.
Ideally, I would like to define 4 different scenarios (x=a, x=b, x=c, x=d). Depending on the scenario, some different actions should be done.
My problem is that ...
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Definition of priors for GLM
I am building a generalized linear model using the logit function in R using JAGS. Whenever I saw code people only define priors for the parameters of the model, but never for parameters of the ...
sprotte
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How do I incorporate sample size as a random variable in a latent interval censored model
I've got a dataset wherein only every third success in a series of n trials is observed. I had originally used the following Bayesian model:
$$
y_i \sim \mathtt{floor}(z_i / 3) \\
z_i \sim B(n_i, p_z) ...
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rjags - implementing a weighted multivariate normal distribution
I am trying to implement a weighted multivariate normal distribution in JAGS 4.3.0
Here is the code I used:
...
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Is it correct to use the posterior distribution from a Bayesian model in other analysis?
I have written a Bayesian model in JAGS that I use to calculate the growth rates of several plant populations as well as their variance while taking into account the observation error during the ...
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Bayesian model (Gibbs sampling) with frequency data for non-standard distribution
I have the following model.
\begin{align}
X &= D\,(F+S\,(1+G)) \\
D &\sim \mathrm{Exp}\,(\lambda) \\
F &\sim \mathrm{Unif}\,(0,1) \\
S &\sim \mathrm{Poi}\,(\sigma) \\
G &\sim \...
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How to make predictions with parameters obtained from a zero-inflated Poisson model with Bayesian approach?
I have a dataset with one response variable and one explanatory variable. Here is my zero-inflated Poisson model with a Bayesian approach.
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Arithmetic using summary means of the MCMC chains, differ from when I do the arithmetic directly using each row of the MCMC chains
I am trying to calculate the 'absolute risk difference' and 'needed to treat' (NNT). NNT is 1/(absolute risk difference) where the absolute risk difference is just the rate of an event in one ...
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Bayesian modeling of data at different cluster level
I have the following data:
The response variable Y and the independent variable X2 were collected at state level (lowest level), while X1 are only available at country level.
The general form of my ...
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RJAGS - Zero Inflated Negative Binomial RJAGS
I am trying to fit a RJAGS zero-inflated negative binomial model. The data I am using has 451 observation and only 12 of them have values different to 0, which means that 97% of my observations are 0. ...
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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 ...
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What Bayesian priors are diffuse on the log scale?
I am trying to come up with priors for the intercept and coefficients of a Bayesian regression model on the log scale, but I can't get the resulting distribution of the prior to be diffuse once I log ...
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Predicting from a Gamma Hurdle Model in JAGS
I fit a gamma hurdle model to invertebrate biomass data and am having trouble predicting from the model. I've been using these posts extensively to try to set it up:
Gamma hurdle model for continuous ...
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Why am I getting very different results from jags and stan
Why am I getting different results from jags and stan in a simple linear regression model?
Using Sepal.Length ~ Petal.Length in the ...
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Bayesian Multiple Regression with Interaction
I want to perform a Bayesian analysis (multivariate regression with an interaction term) using this model:
DEP ~ beta0 + beta1X1 + beta2X2 + beta3(X1X2)**
DEP = Dependent variable (continuous)
X1 = ...
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Bayesian Inference with a Normal Likelihood and Gamma Prior
I am fairly new to (the eye-opening world of) Bayesian inference and machine learning and I am stuck at a inference problem that is important to my current research, where I want to introduce Bayesian ...
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JAGS: predicting missing values in a **discrete** covariate
I am fitting a hierarchical model with NAs in my predictors. What I understand from Kéry & Royle Vol. 1, p. 175 is that, for a continuous predictor, you can model the NAs by fitting a normal ...
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How to correctly include an offset in a bernouli model in JAGS/WINBUGS (Only one success, but number of choices can change)
I am trying to fit a Bernoulli model in Jags. This is for mate choice - whether a male interacts with a given female out of a selection of females. For the purpose of the model they only get one ...
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extracting subset of iteration in jags.samples() in r
Using rjags and jags.samples (), I have multiple parameters that are monitored and some parameters are in matrices. This means for example, with 2 chains and 5000 iterations, jags.samples() may return ...
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JAGS model to PyMC [closed]
I'm trying to translate a code that I have written in JAGS to PyMC but I'm getting stuck due to the recursion in the JAGS code that I can't figure out how to pass it to PyMC.
The model in JAGS is ...
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Hiearchical Beta-Binomial model via rjags: How to draw posterior sample/do inference on posterior exactly?
I have the following code for bugs model which I want to use with rjags:
...
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Why is my 95% CI from a simulation being either 100% or 0%?
So what I intended doing with the code is to simulate dataset with the outcome being number of parasites that died (yi) out of every n=50 parasites on the host. There are 2 treatment groups (control ...
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How to use the parameters estimated by MCMC?
Considering this example, taken from the coursera course "Bayesian Statistics: Techniques and Models",
Dataset:
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Updating prior in MCMC with new estimates for parameters
I'm new to doing Bayesian analysis and I wanted to learn by using baseball data. I took a group of players and found their hits and at bats for various years and I want to be able to get estimates for ...
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influence of bayesian priors: rjags and categorical variables
I am a bad statistician and new using Bayesian tools, and I am learning how to make regressions with rjags. While toying, I came across a situation I do not fully ...
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Jags: Attempt to redefine node error, mixed effect regression [closed]
I want to perform a mixed effect regression in rjags, with a random slope and intercept. I define the following toy dataset:
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Data-informed grouping of covariates in Bayesian Hierarchical Modeling?
Is there a way to place a prior on the first stage's betas that allows the second stage groups to be determined from the data? I am working with co-exposures where I am not super confident in how they ...
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Change shape parameters in a beta distribution based in each datapoint [closed]
I am new to Bayesian statistics and I have been trying to implement a Beta Binomial model from a PhD thesis in rjags. The thesis describes prior distribution for the variables but I am stuck in how to ...
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Bayesian model predictions
I am using an exponential decay model for the prediction of chemical exposures. y[i] is measured exposure, and y_new[i] is a prediction for x_new. I expect that new predictions will have exponential ...
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Does thinning in JAGS/Stan reduce computational time for simulating a chain of a given length?
Question
Let's say we have a complicated model whose posterior distribution we want to draw from using MCMC. To do this, we simulate a chain of total length $N=10,000$. For the sake of this question, ...
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JAGS error in the estimation of a simple INAR model
I am having a hard time try to figure out how to translate a simple INAR(1) model in JAGS.
\begin{equation}
Y_t = \alpha \circ Y_{t-1} + e_t
\end{equation}
where $\circ$ is the binomial thinning ...
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How to model Beta distribution with Uniform prior in RJAGS? [closed]
I'm having trouble modeling a Bayesian problem in RJAGS.
I'm analyzing depth damage curves. In 1988, the US Army Corps of Engineers estimated that 1 foot of flooding would result in 32% mean damage ...
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How to fit a scalable Bayesian VAR model in Stan/JAGS
I am trying to fit a Bayesian vector auto regressive model but I am struggling with the computation. I tried both JAGS and Stan to fit the model but I have never been able to fit it successfully. It ...
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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$) ...
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Distributions with negative support in JAGS
I am creating a Bayesian regression model where I want to include a prior for a variable that can only have a negative coefficient. What distribution can I use that only has a negative support and is ...
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JAGS: Different results when assigning priors to regression coefficients with a for loop than one by one directly
I want to loop through my coefficients to assign them priors, as demonstrated below:
...
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Bayesian sequential updating with current Bayesian sampling software?
I'm having a hard time implementing sequential updating with current software, I don't even know how to start. Basically, I'm having to refit the whole model by simply appending the new data to the ...
user314217
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How to fit a mixture of 2D Gaussian in BUGS/JAGS?
I am trying to estimate the parameters of a mixture of 2D Gaussian distribution using JAGS. I first created two components from a multivariate normal distribution and then combined them to get a ...
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JAGS Cannot insert node into P[1:3,1:100]. Dimension mismatch [closed]
I am new to JAGS and trying to replicate results from a textbook I have been reading. When I run my code I am persistently getting the error
Error in jags.model(model.file, data = data, inits = init....
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Example where the posterior from Jags and Stan are really different and have real impacts on decisions using the model
I have seen many people claiming Stan is "much better" than JAGS, meaning roughly this: "although Jags is much faster, the quality of the samples is worse. So it's worth waiting for the ...
user314217
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Using discrete random variable as index in JAGS
I have a model that can be distilled into,
$$d \sim U(1,20)$$
$$Y_t \sim N(X_{t-d},\sigma^2)$$
for two time series that have observations from $Y_{1,..,t}$ and $X_{1,...,t}$.
I'm trying to code it up ...
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Predict values from complex Rjags model
It's the first time I'm working with R2Jags, MCM chains and Bayesian models and I'm having trouble to compute the predicted values for my model.
The model is based on research by Hallmann et al. 2017, ...
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Is it meaningful for a Bayesian to look at the point estimation of posterior density?
I understand Bayesian look at the quantile. But is it useful to look at the posterior mean and posterior median?
How to explain it in the plain English?
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How to do the sensitive analysis for the prior?
I understand bayesian people always do the sensitive analysis for the study.
However, I am confused how we set the sensitive analysis.
My question is how we set the parameter's for the sensitive ...
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Is it make sense to set the vague prior when your data size is small?
If the data size is small? should we give the noninformative prior(vague prior) to the data set? I think if the data size is small. It is hard for data to tell the whole story.If you do think we can ...
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How to set the gamma prior around the MLE estimate?
I have a MLE estimate for the lambda of the possion distribution.
However, the size of my data sets is very small. I choose use the gamma prior in jags. I want to set my prior around the MLE estimate. ...
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Gibbs updating algorithm (Gibbs steps) for computationally expensive likelihood
I am looking for a good way to update steps in a Gibbs sampler where the likelihood function is computationally expensive. Here is what I tried so far:
By default JAGS uses a slice sampler. However, ...
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Bayesian logistic regression analysis [closed]
I am doing bayesian analysis in which my outcome variable is binary (perinatal mortality). I have a couple of explanatory variables such as gender, birth order, maternal education, residence, maternal ...
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Is there anything wrong with having a Bayesian logistic regression model with beta priors?
The Bayesian logistic regression model with beta priors seem to work using JAGS. I just can't find any examples of it in any literature or any tutorials. They all seem to use normal priors.
Just want ...
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What value of thinning is acceptable in Bayesian data analysis?
I am running a poisson regression in rjags. I observed that my trace plots do not converge very well when I use a thin=10 but they all do converge pretty well when I use a thin=1.
What is the ...
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