├── 10 - Evaluation of model fit and hypothesis testing ├── Discoveries data │ ├── Disease_outbreaks_model.py │ ├── __pycache__ │ │ ├── Disease_outbreaks_model.cpython-36.pyc │ │ └── helper_functions.cpython-36.pyc │ ├── helper_functions.py │ ├── main.py │ └── results │ │ ├── autocorrelation.png │ │ ├── discoveries_hist.png │ │ └── no_discoveries_over_time.png ├── Disease outbreaks │ ├── Disease_outbreaks_model.py │ ├── helper_functions.py │ ├── main.py │ └── results │ │ ├── posterior_gamma38_55.png │ │ ├── posterior_gamma58_65.png │ │ ├── posterior_pred.png │ │ └── prior_gamma305.png ├── __pycache__ │ ├── Disease_outbreaks_model.cpython-36.pyc │ └── helper_functions.cpython-36.pyc └── solutions_chapter_10.pdf ├── 13 - Random Walk Metropolis ├── 13_1_Borrelia │ ├── __pycache__ │ │ ├── assymmetric_model.cpython-36.pyc │ │ └── symmetric_model.cpython-36.pyc │ ├── assymmetric_model.py │ ├── main.py │ ├── results │ │ ├── assymetric_jumping_kernel │ │ │ ├── alpha_posterior.png │ │ │ ├── beta_posterior.png │ │ │ ├── chains.png │ │ │ ├── joint_prior.png │ │ │ └── posterior_alpha_beta_joint.png │ │ └── symmetric_jumping_kernel │ │ │ ├── comparing_step_sizes.png │ │ │ ├── large_step_sz.png │ │ │ ├── lyme_random_walk_metropolis.png │ │ │ ├── lyme_random_walk_metropolis2.png │ │ │ ├── posterior pred │ │ │ ├── p_theta_x_sampled_rwm.png │ │ │ ├── posterior_pred.png │ │ │ └── posterior_pred_RMW.png │ │ │ ├── posterior_pred_ind_sampling.png │ │ │ ├── rmw_100_chains.png │ │ │ ├── rmw_100_chains_burn_in.png │ │ │ ├── samples_conjugate_vs_actual_pdf.png │ │ │ ├── small_step_sz.png │ │ │ ├── small_step_sz_1000it.png │ │ │ ├── small_step_sz_100it.png │ │ │ └── var_conjugate_samples.png │ └── symmetric_model.py ├── 13_3_Malarial_mosquitoes │ ├── main.py │ └── results │ │ ├── chain 1 │ │ ├── chains.png │ │ ├── mu.png │ │ ├── posterior_metropolis_hastongs.png │ │ └── psi.png │ │ ├── chain 2 │ │ ├── chains.png │ │ ├── posterior.png │ │ ├── posterior_mu.png │ │ └── posterior_psi.png │ │ ├── chain 3 │ │ ├── chains.png │ │ ├── mu.png │ │ ├── posterior.png │ │ └── psi.png │ │ ├── chain 4 │ │ ├── chain.png │ │ ├── mu_post.png │ │ ├── posterior.png │ │ └── psi_post.png │ │ └── likelihood.png └── solutions_chapter_13.pdf ├── 14 - Gibbs Sampling ├── 14_1_The sensitivity_and_specificity_of_a_test_for_a_disease │ ├── main.py │ └── results │ │ ├── Q_14_1_3 │ │ └── pi_posterior_uniform_prior.png │ │ ├── Q_14_1_4 │ │ └── pi_posterior_informative_SC_priors.png │ │ ├── Q_14_1_5 │ │ ├── C_posterior.png │ │ ├── Prior.png │ │ └── S_posterior.png │ │ ├── Q_14_1_6 │ │ ├── C_posterior.png │ │ └── S_posterior.png │ │ └── Q_14_1_7 │ │ ├── C_posterior.png │ │ └── S_posterior.png ├── 14_2_ Coal_mining_disasters_in_the_UK │ ├── main.py │ └── results │ │ ├── disasters_yr.png │ │ ├── lambda1_2_posterior_gibbs.png │ │ └── n_posterior_gibbs.png └── solutions_chapter_14.pdf ├── 2 - The subjective worlds of Frequentist ├── 2.3 - Model choice │ └── Q 2.3.1 - 2.3.2 │ │ ├── regression_tests.py │ │ └── results │ │ └── polynomial_fit.png └── solutions_chapter_2.pdf ├── 3 - Probability - the nuts and bolts of Bayesian inference ├── 3.8 - Breast cancer revisited │ ├── Breast_cancer_probability.py │ ├── evaluate_cancer_as_function_false_positive.py │ ├── evaluate_cancer_as_function_prevalence.py │ ├── evaluate_cancer_as_function_true_positive.py │ ├── helper_functions.py │ ├── main.py │ └── results │ │ ├── prob_cancer_given_pos_test_prevalence.png │ │ ├── prob_cancer_given_pos_test_sensitivity_FP.png │ │ └── prob_cancer_given_pos_test_sensitivity_TP.png └── solutions_chap_3.pdf ├── 4 - Likelihood ├── 4.1 - Blog blues │ └── Poisson model │ │ ├── MLE estimate - Evaluate model │ │ ├── __pycache__ │ │ │ ├── helper_functions.cpython-36.pyc │ │ │ └── time_between_blog_visits.cpython-36.pyc │ │ ├── helper_functions.py │ │ ├── main.py │ │ ├── poisson_model.py │ │ └── results │ │ │ └── generated_time_between_beer_visits.png │ │ └── MLE estimate - Mean visit rate │ │ ├── helper_functions.py │ │ ├── main.py │ │ ├── poisson_model.py │ │ └── results │ │ └── likelihood_as_function_of_rate_between_first_time_visits_blog.png ├── 4.2 - Violent crime counts in New York counties │ ├── Evaluate model │ │ ├── MLE_estimator.py │ │ ├── __pycache__ │ │ │ ├── MLE_estimator.cpython-36.pyc │ │ │ └── helper_functions.cpython-36.pyc │ │ ├── helper_functions.py │ │ └── main.py │ ├── MLE Estimate │ │ ├── MLE_estimator.py │ │ ├── helper_functions.py │ │ └── main.py │ ├── __pycache__ │ │ ├── MLE_estimator.cpython-36.pyc │ │ └── helper_functions.cpython-36.pyc │ └── results │ │ └── violent_crime_population.png └── solutions_chapter_4.pdf ├── 5 - Prior └── solutions_chapter_5.pdf ├── 6 - The devil is in the denominator ├── __pycache__ │ ├── helper_functions.cpython-36.pyc │ └── negative_binomial_model.cpython-36.pyc ├── helper_functions.py ├── main.py ├── negative_binomial_model.py ├── results │ └── NB_posterior.png └── solutions_chapter_6.pdf ├── 7 - The posterior ├── Frequentist Models │ ├── __pycache__ │ │ └── helper_functions.cpython-36.pyc │ ├── helper_functions.py │ ├── main.py │ └── results │ │ ├── mortality_GDP.png │ │ ├── mortality_GDP_log.png │ │ └── mortality_GDP_log_normal_dist_model.png ├── MCMC Model │ ├── __pycache__ │ │ └── helper_functions.cpython-36.pyc │ ├── helper_functions.py │ ├── main.py │ └── results │ │ ├── mortality_GDP.png │ │ ├── mortality_GDP_log.png │ │ └── mortality_GDP_log_normal_dist_model.png ├── Prior Predictive │ ├── __pycache__ │ │ └── helper_functions.cpython-36.pyc │ ├── helper_functions.py │ ├── main.py │ └── results │ │ ├── mortality_GDP.png │ │ ├── mortality_GDP_log.png │ │ └── mortality_GDP_log_normal_dist_model.png └── solutions_chapter_7.pdf ├── 8 - Distributions ├── Election Models │ ├── Election_model.py │ ├── __pycache__ │ │ ├── Election_model.cpython-36.pyc │ │ └── helper_functions.cpython-36.pyc │ ├── helper_functions.py │ ├── main.py │ └── results │ │ ├── Multinomial Likelihood More data │ │ ├── likelihood_more_data.png │ │ └── posterior_more_data.png │ │ └── Multinomial Likelihood less data │ │ ├── Dirichlet(1,1,1) prior │ │ ├── dirichlet_prior.png │ │ ├── multinomial_likelihood.png │ │ └── posterior_dirichlet111_prior_multinomial_likelihood.png │ │ └── Dirichlet(10,10,10) prior │ │ ├── multinomial_likelihood.png │ │ ├── posterior_dir_10_10_10.png │ │ └── prior_dir_10_10_10.png ├── __pycache__ │ └── helper_functions.cpython-36.pyc └── solutions_chapter_8.pdf ├── 9 - Conjugate Priors ├── Epilepsy │ ├── Epilepsy_model.py │ ├── __pycache__ │ │ ├── Election_model.cpython-36.pyc │ │ ├── Epilepsy_model.cpython-36.pyc │ │ ├── Lyme_disease_model.cpython-36.pyc │ │ └── helper_functions.cpython-36.pyc │ ├── helper_functions.py │ ├── main.py │ └── results │ │ └── posterior_epilepsy_rate.png ├── Lyme disease │ ├── Lyme_disease_model.py │ ├── __pycache__ │ │ ├── Election_model.cpython-36.pyc │ │ ├── Lyme_disease_model.cpython-36.pyc │ │ └── helper_functions.cpython-36.pyc │ ├── helper_functions.py │ ├── main.py │ └── results │ │ ├── large sample │ │ └── posterior_large_sample.png │ │ ├── posterior predictive │ │ └── posterior_predictive_estimation.png │ │ └── small sample │ │ ├── beta_1_1_prior.png │ │ ├── beta_posterior.png │ │ ├── bin_samp_dist_1_trail.png │ │ └── likelihood_1_trial.png ├── __pycache__ │ └── helper_functions.cpython-36.pyc └── solutions_chapter_9.pdf ├── All_data ├── Eurovision.csv ├── HMC_Uturn.R ├── HMC_scripts.R ├── RWM_mosquito.csv ├── conjugate_epil.csv ├── conjugate_newcomb.csv ├── denominator_NBCoins.csv ├── evaluation_discoveries.csv ├── evaluation_sleepPosteriors.csv ├── evaluation_sleepstudy.csv ├── gibbs_coal.csv ├── glm_eurovision.csv ├── glm_metaAnalysis.csv ├── glm_seatbelts.csv ├── glm_terrorism.csv ├── glm_terrorism_pairwise.csv ├── hierarchical_betaBlocker.csv ├── hierarchical_cancer.csv ├── hierarchical_ode.csv ├── likelihood_NewYorkCrimeUnemployment.csv ├── likelihood_blogVisits.csv ├── linearRegression_crimePunishment.csv ├── posterior_brainData.csv ├── 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