---
# System prepended metadata

title: posteriordb Hackathon

---

# posteriordb Hackathon

2024-08-12

## Participant Guide
- Fork repository: https://github.com/stan-dev/posteriordb
- [Read the docs](https://github.com/stan-dev/posteriordb/tree/master/doc)
- Claim a model from the list below.

For more inforamtion regarding individual models, you can look in the [model information directory](https://github.com/stan-dev/posteriordb/tree/master/posterior_database/models/info) or peek at the [corresponding Stan implementation](https://github.com/stan-dev/posteriordb/tree/master/posterior_database/models/stan).

While the various model output files can be generated automatically for R/Stan users (via the `posteriordb` package), for Python there are some manual steps involved. [Here is a gist](https://gist.github.com/fonnesbeck/a9a0b1624324ee2df30ff32c51e452a8) that tries to create output info and summaries that correspond to the formats currently in the repo. 

If you are looking for some "easy" models to start with, perhaps have a look at the GLM models, which are described in a [population ecology book by Kery and Schaub](https://www.mbr-pwrc.usgs.gov/software/kerybook/BPA_TOC.pdf). There are ecological mark-recapture models (`M0_model`, `Mb_model`, etc.) that are also described in this book.

## Model list

Select an available model from the list:

- [ ] 2pl_latent_reg_irt
- [ ] accel_gp
- [ ] accel_splines
- [ ] arK (Ian Schweer)
- [x] arma11 (Ian Schweer)
- [ ] blr
- [ ] bones_model
- [ ] bym2_offset_only
- [ ] covid19imperial_v2
- [ ] covid19imperial_v3
- [ ] diamonds
- [ ] dogs
- [ ] dogs_hierarchical
- [ ] dogs_log
- [ ] dogs_nonhierarchical
- [ ] dugongs_model
- [ ] earn_height
- [ ] eight_schools_centered
- [x] eight_schools_noncentered (Chris Fonnesbeck)
- [ ] election88_full
- [ ] garch11
- [x] GLM_Binomial_model (Ricardo)
- [x] GLM_Poisson_model
- [ ] GLMM1_model
- [ ] GLMM_Poisson_model
- [ ] gp_pois_regr
- [ ] gp_regr
- [ ] gpcm_latent_reg_irt
- [ ] grsm_latent_reg_irt
- [ ] hier_2pl
- [ ] hierarchical_gp
- [ ] hmm_drive_0
- [ ] hmm_drive_1
- [ ] hmm_example
- [ ] hmm_gaussian
- [ ] iohmm_reg
- [ ] irt_2pl
- [ ] kidscore_interaction
- [ ] kidscore_interaction_c
- [ ] kidscore_interaction_c2
- [ ] kidscore_interaction_z
- [ ] kidscore_mom_work
- [ ] kidscore_momhs
- [ ] kidscore_momhsiq
- [ ] kidscore_momiq
- [ ] kilpisjarvi
- [ ] kronecker_gp
- [ ] ldaK2
- [ ] ldaK5
- [ ] log10earn_height
- [ ] logearn_height
- [ ] logearn_height_male
- [ ] logearn_interaction
- [ ] logearn_interaction_z
- [ ] logearn_logheight_male
- [ ] logistic_regression_rhs
- [ ] logmesquite
- [ ] logmesquite_logva
- [ ] logmesquite_logvas
- [ ] logmesquite_logvash
- [ ] logmesquite_logvolume
- [ ] losscurve_sislob
- [ ] lotka_volterra
- [ ] low_dim_gauss_mix
- [ ] low_dim_gauss_mix_collapse
- [ ] lsat_model
- [x] M0_model (Chris Fonnesbeck)
- [ ] Mb_model
- [ ] mesquite
- [ ] Mh_model
- [ ] Mt_model
- [ ] Mtbh_model
- [ ] Mth_model
- [ ] multi_occupancy
- [ ] nes
- [ ] nes_logit_model
- [ ] nn_rbm1bJ10
- [ ] nn_rbm1bJ100
- [ ] normal_mixture
- [ ] normal_mixture_k
- [ ] one_comp_mm_elim_abs
- [ ] pilots
- [ ] prophet
- [ ] radon_county
- [ ] radon_county_intercept
- [ ] radon_hierarchical_intercept_centered
- [ ] radon_hierarchical_intercept_noncentered
- [ ] radon_partially_pooled_centered
- [ ] radon_partially_pooled_noncentered
- [ ] radon_pooled
- [ ] radon_variable_intercept_centered
- [ ] radon_variable_intercept_noncentered
- [ ] radon_variable_intercept_slope_centered
- [ ] radon_variable_intercept_slope_noncentered
- [ ] radon_variable_slope_centered
- [ ] radon_variable_slope_noncentered
- [ ] Rate_1_model
- [ ] Rate_2_model
- [ ] Rate_3_model
- [ ] Rate_4_model
- [ ] Rate_5_model
- [ ] rats_model
- [ ] seeds_centered_model
- [ ] seeds_model
- [ ] seeds_stanified_model
- [ ] sesame_one_pred_a
- [ ] sir
- [ ] soil_incubation
- [ ] state_space_stochastic_level_stochastic_seasonal
- [ ] surgical_model
- [ ] Survey_model
- [ ] wells_daae_c_model
- [ ] wells_dae_c_model
- [ ] wells_dae_inter_model
- [ ] wells_dae_model
- [ ] wells_dist
- [ ] wells_dist100_model
- [ ] wells_dist100ars_model
- [ ] wells_interaction_c_model
- [ ] wells_interaction_model
