# Questions tagged [hierarchical-bayesian]

Hierarchical Bayesian models specify priors on parameters and hyperpriors on the parameters of the prior distributions

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### Pro and cons between Bayesian structural time series (BSTS) vs difference-in-differences?

Google's paper markets BSTS's benefits over DID such that "In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of ...
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### Hierarchical Version of Bayesian Change Detection Model in JAGS

I am trying to create a hierarchical changepoint detection model in JAGS, estimating group difference in changepoint based on individual changepoints in scores for an outcome variable (fictional in ...
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### Bayesian modeling: likelihood function for continuous random variables, why is it not always 0? [duplicate]

For continuous random variables, evaluating p(x) for a specific value of x is always 0 as show here, here and here. So when we're calculating the likelihood for a random variable X that is represented ...
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### Bayesian inference on mean of statistic from population

Suppose that a collection of time intervals $t_i$ have occurred, for $i=1,...,n$. These should be considered as samples from a population governed by some distribution. During these time intervals, ...
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### sampling from a posterior derived from hierarchical Bayesian using HMC

I have a complex pdf based on hierarchical Bayesian formalism where x depends on the priors w'and w'', and I consider hyper-prior for the latter's as w=Php(zeta,beta) where Php stands for the hyper ...
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### Dependence between parameters in Bayesian multilevel regression

I am working on a Bayesian multilevel regression model, which is specified as $$y_{ij}=x_{ij}'\beta+\delta_j+\varepsilon_{ij}\\ \delta_j=\gamma_{\operatorname{region}(j)}+\eta_j$$ where $i$ indexes ...
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### Hierarchical Bayesian Modelling: deriving a Conditional PDF relation

Background I am currently reading through the book: Data Analysis: A Bayesian Tutorial, second edition by D.S. Sivia. I am stumped by one of the relations that he introduces in the context of model ...
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### Hierarchical modelling - partial pooling with correlation

I am doing a Bayesian regression. I have groups of data $(y_1 ~X_1), (y_2~X_2),...$, where each $y$ and $X$ is a vector. The subscript is regarded as group number. The completely unpooled regression ...
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### Approaches to fast estimation of new levels of a hierarchical linear model from new data

I have a hierarchical linear model I've applied to a dataset in which the effect of a factor on my outcome measure can vary for different people. Say I have a new individual for whom I have some ...
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### Does a closed form marginal posterior exist for regression coefficients in hierarchical linear model?

Given the standard linear model $$Y=X\beta+\epsilon$$ a Gaussian likelihood function $Y|b,\sigma^2 \sim N(X\beta,\sigma^2 I)$ and a hierarchical model for the regression coefficient $\beta$ of a form ...
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### Hierarchical (multilevel, random-effects) Gaussian process regression

If we have a $J$ groups of predictor, outcome (univariate) variable pairs, $$\{(y_{j1}, x_{j1}) \ldots (y_{jn_j}, x_{jn_j})\}, \quad\text{for j \in 1\cdots J},$$ a hiearchical linear regression ...
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### Is this correct hierarchical Bernoulli model?

I have a question about correctness of a model that I used for a fairly simple experiment. I'm not sure if it should go to stackoverflow or crossvalidated, because I feel like my question is both ...
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### Discrete predictors in a mixed effects model in JAGS

I'm fitting a random effects hierarchical model in JAGS and have a question regarding discrete predictors in the contexts of mixed effects models. In my data there is a group variable, which is the ...