Absolutely — allways identify parameters where you can for exactly the reasons T mentions. As long as the prior is zero mean, tau doesn’t strictly need to be constrained to be positive (if tau changes sign, one can change the signs of theta_step to give the same theta). . Using Laplace (double exponential) priors, although analagous to the the lasso, is not an example. I don't understand a couple of things here. These are the choices we used here. They introduce a new version of the horseshoe prior, the regularized horseshoe prior that looks like this, \[ Electronic Journal of Statistics 11(2):5018-5051. doi: 10.1214/17-EJS1337SI↩, Note: You can check this easily using prior_summary(). The horseshoe prior is a member of the family of multivariate scale mixtures of normals, and is therefore closely related to widely used ap- proaches for sparse Bayesian learning, includ- ing, among others, Laplacian priors (e.g. If you really want to know and understand horseshoe priors, you’ll need to read a paper by Juho Pironen and Aki Vehtari in the Electronic Journal of Statistics, 1 but here’s a brief outline.. \tilde\lambda_i &=& \frac{c^2\lambda_i^2}{c^2 + \tau^2\lambda_i^2} \\ lambda_step is Cauchy(0,1), so lambda computed as product of lambda_step and tau is Cauchy(0, tau), which is how lambda is defined in the model. We discussed horseshoe in Stan awhile ago, and there’s more to be said on this topic, including the idea of postprocessing the posterior inferences if there’s a desire to pull some coefficients all the way to zero. According to the original publication: > its flat, Cauchy-like tails allow strong signals to remain large […] > a posteriori. Just imagine how much trickier it would be if the true relationship were non-linear rather than linear. And why lambda can be calculated as a product of lambda_step and tau? \beta_i &\sim& \mbox{N}(0, \tau^2\lambda_i^2) \\ The nice thing about “horseshoe priors” in rstanarm is that if you know how to set up a regression in stan_glm() or stan_glmer() you can use a horseshoe prior very easily in your analysis simply by changing the prior parameter in your call to one of those functions. Why tau isn't constrained to be positive? The basic horsehoe prior affects only the last of these. The likelihood in standard linear regression model looks like this This may, however, lead to an increased number of … Requires use of STAN command file multilevel.stan. As Pironen and Vehtari explain, however, there hasn’t been consensus on how to set or estimate \(\tau\). Read Pironen and Vehtari if you want all of the gory details. The horseshoe, just as the LASSO, can be used when the slopes are assumed to be sparse. There are several things I like about using regularized horeshoe priors in rstanarm rather than the Lasso. . Well you can see a few of the details in the next section, or you can just skip to the section where we use it in rstanarm if you find the math more confusing than helpful. The key parameter tau can be equipped with It won’t come as a surprise to anyone who knows me that I have to try a Bayesian approach to variable selection. A special shrinkage prior to be applied on population-level effects is the (regularized) horseshoe prior and related priors. \eqalign{ Using this Bayesian approach, however, we can see that even though x3 isn’t “significant” in the analysis of the first data set and is in the second,5 we don’t have good evidence that the estimates are different, because the posterior distributions are broadly overlapping as evidenced by the broadly overlapping credible intervals. \beta_i &\sim& \mbox{N}(0, 2.5) \quad \mbox{for } i > 0 A Bayesian competitor to the Lasso makes use of the “Horseshoe prior” (which I’ll call “the Horseshoe” for symmetry). Tomi Peltola, Aki Havulinna, Veikko Salomaa, and Aki Vehtari write: This paper describes an application of Bayesian linear survival regression . If you really want to know and understand horseshoe priors, you’ll need to read a paper by Juho Pironen and Aki Vehtari in the Electronic Journal of Statistics,1 but here’s a brief outline. 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