This repository contains data and models to produce posteriors based on different probabilistic programming languages (PPL). Currently, the focus is Stan, but it should be possible to use it with other frameworks as well.
There are many purposes with the PDB
- A simple repository to access many models and datasets in a structured way from R and Python
- Store models and data in a structure that lends itself to testing inference algorithms on a large number of posteriors.
- An interface that makes it easy for students to access models and data for courses in Bayesian data analysis.
- Model contents that are framework agnostic (although now Stan is in focus), and can be used with many different probabilistic programming frameworks.
- A structure that simplifies regression testing of probabilistic programming frameworks.
- Providing reliable reference posteriors for use in inference method development.
The long term goal is to move the posterior database to an open RESTful NoSQL database for easy access.
See DATABASE_CONTENT.md for the details content of the posterior database.
We are happy with any help in adding posteriors, data, and models to the database! See CONTRIBUTING.md for the details on how to contribute.
Install the package from GitHub
remotes::install_github("MansMeg/posteriordb", subdir = "rpackage")Load the R package and load a posterior from the default posteriordb.
library(posteriordb)
pd <- pdb_default() # Posterior database connection
pn <- posterior_names(pd)
head(pn)## [1] "arK-arK" "arma-arma11"
## [3] "bball_drive_event_0-hmm_drive_0" "bball_drive_event_1-hmm_drive_1"
## [5] "butterfly-multi_occupancy" "diamonds-diamonds"
po <- pdb_posterior("eight_schools-eight_schools_centered", pdb = pd)
po## Posterior (eight_schools-eight_schools_centered)
##
## Data: eight_schools
## The 8 schools dataset of Rubin (1981)
##
## Model: eight_schools_centered
## A centered hiearchical model for 8 schools
## Frameworks: 'stan', 'pymc3'
From the posterior, we can easily access data and models as
sc <- pdb_stan_code(x = po)
sc## data {
## int <lower=0> J; // number of schools
## real y[J]; // estimated treatment
## real<lower=0> sigma[J]; // std of estimated effect
## }
## parameters {
## real theta[J]; // treatment effect in school j
## real mu; // hyper-parameter of mean
## real<lower=0> tau; // hyper-parameter of sdv
## }
## model {
## tau ~ cauchy(0, 5); // a non-informative prior
## theta ~ normal(mu, tau);
## y ~ normal(theta, sigma);
## mu ~ normal(0, 5);
## }
We can get additional information about the model by using info().
info(sc)## Model: eight_schools_centered
## A centered hiearchical model for 8 schools
## Frameworks: 'stan', 'pymc3'
To access data for a specific posterior, we can use pdb_data()
dat <- pdb_data(po)
dat## $J
## [1] 8
##
## $y
## [1] 28 8 -3 7 -1 1 18 12
##
## $sigma
## [1] 15 10 16 11 9 11 10 18
Again, we can get additional information about the data by using
info().
info(dat)## Data: eight_schools
## The 8 schools dataset of Rubin (1981)
Finally, we can access reference posterior draws for the given posterior.
rpd <- reference_posterior_draws(po)The posterior is based on the
posterior R package structure and
can easily be summarized and transformed using the posterior R
package.
library(posterior)## This is posterior version 0.1.2
summarize_draws(rpd)## # A tibble: 10 x 10
## variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 theta[1] 6.15 5.59 5.62 4.56 -1.68 16.3 1.00 10095. 9732.
## 2 theta[2] 4.94 4.77 4.65 4.14 -2.22 12.8 1.00 10049. 10139.
## 3 theta[3] 3.91 4.11 5.28 4.48 -4.91 11.8 1.00 9533. 9339.
## 4 theta[4] 4.80 4.70 4.77 4.22 -2.67 12.6 1.00 10026. 9666.
## 5 theta[5] 3.61 3.82 4.61 4.15 -4.26 10.6 1.00 9922. 10207.
## 6 theta[6] 4.05 4.16 4.80 4.32 -3.87 11.5 1.00 9783. 10039.
## 7 theta[7] 6.32 5.80 5.00 4.39 -0.855 15.3 1.00 10039. 9690.
## 8 theta[8] 4.88 4.79 5.32 4.47 -3.32 13.5 1.00 9605. 9871.
## 9 mu 4.41 4.36 3.31 3.30 -0.936 9.83 1.00 10041. 9973.
## 10 tau 3.60 2.75 3.20 2.55 0.257 9.73 1.00 9989. 9992.
Using info(), we can access more detailed information on the reference
posterior draws.
info(rpd)## Posterior: eight_schools-eight_schools_noncentered
## Method: stan_sampling (rstan 2.21.1)
## Arguments:
## chains: 10
## iter: 20000
## warmup: 10000
## thin: 10
## seed: 4711
## adapt_delta: 0.95
It is also possible to access only information for models, data, and draws as follows.
pdb_model_info(po)## Model: eight_schools_centered
## A centered hiearchical model for 8 schools
## Frameworks: 'stan', 'pymc3'
pdb_data_info(po)## Data: eight_schools
## The 8 schools dataset of Rubin (1981)
pdb_reference_posterior_draws_info(po)## Posterior: eight_schools-eight_schools_noncentered
## Method: stan_sampling (rstan 2.21.1)
## Arguments:
## chains: 10
## iter: 20000
## warmup: 10000
## thin: 10
## seed: 4711
## adapt_delta: 0.95
See python README
See R README
The main focus of the database is simplicity, both in understanding and in use.
The following are the current design choices in designing the posterior database.
- Priors are hardcoded in model files as changing the prior changes the posterior. Create a new model to test different priors.
- Data transformations are stored as different datasets. Create new data to test different data transformations, subsets, and variable settings. This design choice makes the database larger/less memory efficient but simplifies the analysis of individual posteriors.
- Models and data has (model/data).info.json files with model and data specific information.
- Templates for different JSONs can be found in content/templates and schemas in schemas (Note: these don’t exist right now and will be added later)
- Prefix ‘syn_’ stands for synthetic data where the generative process is known and found in content/data-raw.
- All data preprocessing is included in content/data-raw.
- Specific information for different PPL representations of models is included in the PPL syntax files as comments, not in the model.info.json files.