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First of all, thank you very much for this great package! It is really nice to have an easy to use R package to generate designs for choice experiments.
I would like to use the CEA procedure to gen…
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@strengejacke @DominiqueMakowski Let's open this up here officially.
I think this paper can be quite short, really. Here I am pre-regestering my hypotheses:
1. For posterior-based indices (which i…
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Currently the classification and regression algorithms in the `neighbors` module use a flat prior. They should be modified to compute a prior based on training data, and to optionally accept a user-d…
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**Describe the solution you'd like**
When estimating a Bayesian Regression model using `brms` you can select the option `sample_prior = "yes"` in order to also sample from the priors, in addition to …
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Hi,
I am trying to use tensorflow probability to learn a Bayesian neural networks. I want to learn the responses y_t based on input features x_t, i.e.
` y_t = f(x_t) + eps`
where f(x_t) is th…
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## Rough ideas, need vetting.
- reference CTMC
- [Solving the Forecast Combination Puzzle](https://arxiv.org/pdf/2308.05263.pdf) via log-linear Hyvaarinen stacking (locking).
- Quality of sp…
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It would be nice to add informed priors to Bayesian correlation. For example, setting both alpha and beta in a stretched beta distribution. Currently, only zero-centered priors are possible.
Today,…
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Hello Bayesian Wizards,
I recently worked on something for which the client was not sure about the adstock decay rates.
They said it should be somewhere between 30 to 50% for each of the 5 channels.…
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There is a decent amount of research using Bayesian NN layers with the prior set to the posterior initialization (or a function thereof): see e.g. Dziugaite & Roy, 2017. I am currently using this meth…
biggs updated
4 years ago
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Packages such as rstanarm and brms permit glm/glmer-like formula-based model specification, but also include default and specifiable priors on model parameters that should be fairly straightforward to…