**tarakc02 + bayesian-inference**
6

Bayes Sparse Regression

december 2018 by tarakc02

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

june 2018 by tarakc02

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

august 2017 by tarakc02

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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