Add 68 idiomatic PyMC model implementations

posterior_database/models/info/GLMM1_model.info.json

@@ -1,6 +1,10 @@
 {
   "name": "GLMM1_model",
-  "keywords": ["BPA", "Ch.4", "GLMM1_model"],
+  "keywords": [
+    "BPA",
+    "Ch.4",
+    "GLMM1_model"
+  ],
   "title": "Generalized Linear Mixed Model for Peregrine Population Size",
   "description": "A GLMM for modeling peregrine population size with a random effect being the site of observation.",
   "urls": "https://github.com/stan-dev/example-models/blob/master/BPA/Ch.04",
@@ -11,6 +15,9 @@
     "stan": {
       "model_code": "models/stan/GLMM1_model.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/GLMM1_model.py"
     }
   },
   "licence": "BSD3"

posterior_database/models/info/GLMM_Poisson_model.info.json

@@ -1,6 +1,10 @@
 {
   "name": "GLMM_Poisson_model",
-  "keywords": ["BPA", "Ch.4", "GLMM_Poisson_model"],
+  "keywords": [
+    "BPA",
+    "Ch.4",
+    "GLMM_Poisson_model"
+  ],
   "title": "Mixed Model to Predict Population Size with Random Site and Year Effects",
   "description": "A GLMM for modeling peregrine population size with a random effect being the site and year of observation using the Poisson distribution to model counts.",
   "urls": "https://github.com/stan-dev/example-models/blob/master/BPA/Ch.04",
@@ -11,6 +15,9 @@
     "stan": {
       "model_code": "models/stan/GLMM_Poisson_model.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/GLMM_Poisson_model.py"
     }
   },
   "licence": "BSD3"

posterior_database/models/info/GLM_Poisson_model.info.json

@@ -1,6 +1,12 @@
 {
   "name": "GLM_Poisson_model",
-  "keywords": ["Population", "Peregrine", "Generalized", "Generalised", "Linear Model"],
+  "keywords": [
+    "Population",
+    "Peregrine",
+    "Generalized",
+    "Generalised",
+    "Linear Model"
+  ],
   "title": "Poisson GLM for modeling a population of Peregrines",
   "description": "Poisson Generalised Linear Model where the linear predictor is a cubic polynomial function of time.",
   "urls": "https://github.com/stan-dev/example-models/tree/master/BPA/Ch.03",
@@ -11,6 +17,9 @@
     "stan": {
       "model_code": "models/stan/GLM_Poisson_model.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/GLM_Poisson_model.py"
     }
   },
   "licence": "BSD3"

posterior_database/models/info/Rate_2_model.info.json

@@ -1,6 +1,11 @@
 {
   "name": "Rate_2_model",
-  "keywords": ["Trial", "Success Rate", "Beta Distribution", "Difference"],
+  "keywords": [
+    "Trial",
+    "Success Rate",
+    "Beta Distribution",
+    "Difference"
+  ],
   "title": "Difference in success rates",
   "description": "Predicting the difference between the success rates of two trials",
   "urls": "https://github.com/stan-dev/example-models/tree/master/Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial",
@@ -11,6 +16,9 @@
     "stan": {
       "model_code": "models/stan/Rate_2_model.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/Rate_2_model.py"
     }
   },
   "licence": "BSD3"

posterior_database/models/info/Rate_4_model.info.json

@@ -1,6 +1,12 @@
 {
   "name": "Rate_4_model",
-  "keywords": ["Trial", "Success Rate", "Inference", "Prior Inference", "Posterior Inference"],
+  "keywords": [
+    "Trial",
+    "Success Rate",
+    "Inference",
+    "Prior Inference",
+    "Posterior Inference"
+  ],
   "title": "Success rate of a trial with prior and posterior inference",
   "description": "Inference of the success rate of a trial with prior and posterior inference",
   "urls": "https://github.com/stan-dev/example-models/tree/master/Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial",
@@ -11,6 +17,9 @@
     "stan": {
       "model_code": "models/stan/Rate_4_model.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/Rate_4_model.py"
     }
   },
   "licence": "BSD3"

posterior_database/models/info/Rate_5_model.info.json

@@ -1,6 +1,11 @@
 {
   "name": "Rate_5_model",
-  "keywords": ["Trial", "Correct Answer Rate", "Beta Distribution", "Common Rate"],
+  "keywords": [
+    "Trial",
+    "Correct Answer Rate",
+    "Beta Distribution",
+    "Common Rate"
+  ],
   "title": "Common Rate of Success From Two Trials with Posterior Predictives",
   "description": "Inference of a common rate of success from two trials with posterior predictive for each trial for checking the adequacy of the model.",
   "urls": "https://github.com/stan-dev/example-models/tree/master/Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial",
@@ -11,6 +16,9 @@
     "stan": {
       "model_code": "models/stan/Rate_5_model.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/Rate_5_model.py"
     }
   },
   "licence": "BSD3"

posterior_database/models/info/accel_splines.info.json

@@ -1,6 +1,9 @@
 {
   "name": "accel_splines",
-  "keywords": ["stan_benchmark", "spline"],
+  "keywords": [
+    "stan_benchmark",
+    "spline"
+  ],
   "title": "Splines for time-series data with varying mean and std",
   "prior": {
     "keywords": "stan_recommended_35dbfe6"
@@ -11,6 +14,9 @@
     "stan": {
       "model_code": "models/stan/accel_splines.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/accel_splines.py"
     }
   },
   "references": "bales2019selecting",

posterior_database/models/info/arK.info.json

@@ -2,7 +2,10 @@
   "name": "arK",
   "title": "Autoregressive-5 model",
   "description": "An AR(5) time series model.",
-  "keywords": ["time series", "stan_benchmark"],
+  "keywords": [
+    "time series",
+    "stan_benchmark"
+  ],
   "urls": "https://github.com/stan-dev/stat_comp_benchmarks/tree/master/benchmarks/arK",
   "prior": {
     "keywords": []
@@ -11,6 +14,9 @@
     "stan": {
       "model_code": "models/stan/arK.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/arK.py"
     }
   },
   "added_by": "Mans Magnusson",

posterior_database/models/info/blr.info.json

@@ -12,6 +12,9 @@
     "stan": {
       "model_code": "models/stan/blr.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/blr.py"
     }
   },
   "added_by": "Mans Magnusson",

posterior_database/models/info/diamonds.info.json

@@ -1,6 +1,9 @@
 {
   "name": "diamonds",
-  "keywords": ["stan_benchmark", "linear regression"],
+  "keywords": [
+    "stan_benchmark",
+    "linear regression"
+  ],
   "title": "Multiple Highly Correlated Predictors Log-Log Model",
   "prior": {
     "keywords": "stan_recommended_35dbfe6"
@@ -11,6 +14,9 @@
     "stan": {
       "model_code": "models/stan/diamonds.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/diamonds.py"
     }
   },
   "references": "bales2019selecting",

posterior_database/models/info/dogs.info.json

@@ -1,6 +1,10 @@
 {
   "name": "dogs",
-  "keywords": ["ARM", "Ch. 24", "stan_examples"],
+  "keywords": [
+    "ARM",
+    "Ch. 24",
+    "stan_examples"
+  ],
   "title": "Logistic Mixed Effects Model",
   "prior": {
     "keywords": "stan_recommended_35dbfe6"
@@ -11,6 +15,9 @@
     "stan": {
       "model_code": "models/stan/dogs.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/dogs.py"
     }
   },
   "references": "gelman2006data",

posterior_database/models/info/dogs_hierarchical.info.json

@@ -1,6 +1,9 @@
 {
   "name": "dogs_hierarchical",
-  "keywords": ["ARM", "Ch. 24"],
+  "keywords": [
+    "ARM",
+    "Ch. 24"
+  ],
   "title": "Hierarchical Logistic Mixed Effects Model",
   "prior": {
     "keywords": "stan_recommended_35dbfe6"
@@ -14,6 +17,9 @@
     "stan": {
       "model_code": "models/stan/dogs_hierarchical.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/dogs_hierarchical.py"
     }
   },
   "licence": "BSD3"

posterior_database/models/info/dugongs_model.info.json

@@ -1,6 +1,10 @@
 {
   "name": "dugongs_model",
-  "keywords": ["nonlinear", "non-linear", "growth curve"],
+  "keywords": [
+    "nonlinear",
+    "non-linear",
+    "growth curve"
+  ],
   "title": "Dugong Age and Length",
   "description": "Model of age and length data of dugongs using a nonlinear growth curve with no inflection point.",
   "urls": "https://github.com/stan-dev/example-models/tree/master/bugs_examples/vol2",
@@ -11,6 +15,9 @@
     "stan": {
       "model_code": "models/stan/dugongs_model.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/dugongs_model.py"
     }
   },
   "licence": "BSD3"

posterior_database/models/info/earn_height.info.json

@@ -1,6 +1,10 @@
 {
   "name": "earn_height",
-  "keywords": ["ARM", "Ch. 4", "stan_examples"],
+  "keywords": [
+    "ARM",
+    "Ch. 4",
+    "stan_examples"
+  ],
   "title": "One Predictor Linear Model",
   "prior": {
     "keywords": "stan_recommended_35dbfe6"
@@ -11,6 +15,9 @@
     "stan": {
       "model_code": "models/stan/earn_height.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/earn_height.py"
     }
   },
   "references": "gelman2006data",

posterior_database/models/info/eight_schools_centered.info.json

@@ -2,8 +2,14 @@
   "name": "eight_schools_centered",
   "title": "A centered hiearchical model for 8 schools",
   "description": "A centered hiearchical model for the 8 schools example of Rubin (1981)",
-  "keywords": ["bda3_example", "hiearchical"],
-  "references": ["rubin1981estimation", "gelman2013bayesian"],
+  "keywords": [
+    "bda3_example",
+    "hiearchical"
+  ],
+  "references": [
+    "rubin1981estimation",
+    "gelman2013bayesian"
+  ],
   "urls": "http://www.stat.columbia.edu/~gelman/arm/examples/schools",
   "prior": {
     "keywords": []
@@ -12,6 +18,9 @@
     "stan": {
       "model_code": "models/stan/eight_schools_centered.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/eight_schools_centered.py"
     }
   },
   "added_by": "Mans Magnusson",

posterior_database/models/info/election88_full.info.json

@@ -1,6 +1,10 @@
 {
   "name": "election88_full",
-  "keywords": ["ARM", "Ch. 14", "stan_examples"],
+  "keywords": [
+    "ARM",
+    "Ch. 14",
+    "stan_examples"
+  ],
   "title": "Generalized Linear Mixed Effects Model",
   "prior": {
     "keywords": "stan_recommended_35dbfe6"
@@ -11,6 +15,9 @@
     "stan": {
       "model_code": "models/stan/election88_full.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/election88_full.py"
     }
   },
   "references": "gelman2006data",

posterior_database/models/info/gp_pois_regr.info.json

@@ -2,7 +2,10 @@
   "name": "gp_pois_regr",
   "title": "Gaussian Process Poisson Regression",
   "description": "Poisson regression with a one-dimensional latent Gaussian process \nusing the exponential quadratic covariance function.",
-  "keywords": ["stan_benchmark", "gaussian process"],
+  "keywords": [
+    "stan_benchmark",
+    "gaussian process"
+  ],
   "references": null,
   "urls": "https://github.com/stan-dev/stat_comp_benchmarks/tree/master/benchmarks/gp_pois_regr",
   "prior": {
@@ -12,6 +15,9 @@
     "stan": {
       "model_code": "models/stan/gp_pois_regr.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/gp_pois_regr.py"
     }
   },
   "added_by": "Mans Magnusson",

posterior_database/models/info/gp_regr.info.json

@@ -2,7 +2,10 @@
   "name": "gp_regr",
   "title": "Gaussian Process regression",
   "description": "One-dimensional Gaussian process Regression using the\nexponential quadratic covariance function.",
-  "keywords": ["stan_benchmark", "gaussian process"],
+  "keywords": [
+    "stan_benchmark",
+    "gaussian process"
+  ],
   "references": null,
   "urls": "https://github.com/stan-dev/stat_comp_benchmarks/tree/master/benchmarks/gp_regr",
   "prior": {
@@ -12,6 +15,9 @@
     "stan": {
       "model_code": "models/stan/gp_regr.stan",
       "stan_version": ">=2.26.0"
+    },
+    "pymc": {
+      "model_code": "models/pymc/gp_regr.py"
     }
   },
   "added_by": "Mans Magnusson",