tarakc02 + bayesian-inference   6

Bayes Sparse Regression
In this case study I’ll review how sparsity arises in frequentist and Bayesian analyses and discuss the often subtle challenges in implementing sparsity in practical Bayesian analyses.
bayesian-inference  sparsity  sparse-regression  regression  statistical-modeling  bayes  shrinkage  penalized-regression  lasso 
december 2018 by tarakc02
Visualization in Bayesian workflow
Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high-dimensional models that are used by applied researchers.
bayes  dataviz  stats  bayesian-inference  statistics  workflow  bayesian-workflow  posterior-predictive-checks 
june 2018 by tarakc02
brms: An R Package for Bayesian Multilevel Models Using Stan | Bürkner | Journal of Statistical Software
The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit - among others - linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include autocorrelation of the response variable, user defined covariance structures, censored data, as well as meta-analytic standard errors. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with the Watanabe-Akaike information criterion and leave-one-out cross-validation.
bayesian-inference  multilevel-model  ordinal-data  MCMC  stan  Rstats  bayesian  rstan  hierarchical-models 
august 2017 by tarakc02

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