Questions tagged [hierarchical-bayesian]

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

433 questions
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Interpreting the results of hierarchical bayes model including one covariant using ChoiceModelR

There are 5 different features of a product (in this case gyms): ambiente, trainingstools offered, atmosphäre, gastronomie and location. Within the participants there are people who train on a ...
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How to fit newer cohorts using Pareto/NBD or Beta/Geo for CLTV

I am trying to fit the popular Pareto/NBD or Beta/Geometric models for non-contractual, continuous customer data. On top of that I then fit the Gamma/Gamma model for monetary value (using the very ...
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I have this 3 clustering algorithms and I want to figure out which algorithm has the best algorithm for clustering

I'm new with clustering. I have this 3 algorithms and I want to figure out which algorithm has the best algorithm for clustering. I posted an image below, to show my clusters. I am confused on how to ...
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sampling behind bayesian hierarchical models

I'm unsure how sampling is done in Bayesian Hierarchical modelling, i'm reading a book on how to use it in PyMC3 but it doesn't explain the math and i'd like to understand it. Suppose i want to ...
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How to use Selective Bayesian Forest Classifier (SBFC) in R?

I came across with this r package 'sbfc'. It appears very interesting as they talk about Bayesian Forest and it competes with random forest performance. If someone can walk me through this package ...
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Using PyMC3, how could I force a maximum to posterior distribution?

I am pretty new to bayesian statistics and PyMC3. I am doing a hierarchical model where the output variable I am trying to predict is a percentage with a maximum of 100%. My problem is that my ...
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Bayesian Modeling: Yes, No and Maybe Responses

Respondents replied in the following way: Yes: they will be attending No: they won't be attending Maybe: they attach a percentage certainty as an estimate that they'll be attending. E.g. 40% sure ...
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Hierarchial Bayesian approaches versus simple prior based approaches

The point of Hierarchical Bayesian models is that you can get parameters for different "hierarchies" within your data. For example, if you have 10 data points for one person, 10 for the next and so on,...
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Understanding covariance in Bayesian regression model

I am confused about when to model covariance in a Bayesian regression. Here's what I am trying to model. I have a dataset which has scores for a set of students who did a set of practice exam problems....
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Applications of Hierarchical Dirichlet Process to Continuous Data

I read Yee Whye Teh et al.'s paper on Hierarchical Dirichlet Process. In section 5, they show sampling algorithm using base distribution H and data distribution F. One of their applications is HDP-...
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Problem with “log(0)” error while using brms in R to do Bayesian analysis [closed]

I'm using brms to conduct a multilevel regression in R. I've been getting warnings and errors of the following type: ...
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Selecting Bayesian priors for the dependent data

I have goal of measuring CTRs of several titles of an article on a website using Bayesian approach. In a simple setup, what one will do is to select Beta Prior (for example Uniform distribution) and ...
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Hierarchical linear modelling in R

I am trying to build a hierarchical linear model based on data structured like this dataset below. The model form I am looking to build is Purchased ~ f(price + color + more item attributes + age + ...
From the compound sampling model where: $Y_i | \theta_i \sim Poi(\theta_i)$ The marginal distribution of $\theta_i$ is $G$, non-parametric. We get that the Bayes estimate of $\theta_i$ under ...