BE/Bi 103 b: Statistical Inference in the Biological Sciences ============================================================= In the `prequel to this course `_, we developed tools to build data analysis pipelines, including the organization, preservation, sharing, and display quantitative data. We also learned basic techniques in statistical inference using resampling methods taking a frequentist approach. In this class, we go deeper into statistical modeling and inference, mostly taking a Bayesian approach. We discuss generative modeling, parameter estimation, model comparison, hierarchical modeling, Markov chain Monte Carlo, graphical display of inference results, and principled workflows. All of these topics are explored through analysis of real biological data sets. If you are enrolled in the course, please read the :ref:`Course policies` below. We will not go over them in detail in class, and it is your responsibility to understand them. Useful links ------------- - `Ed `_ (used for course communications) - `Course Zoom link `_ (password protected) - `Google doc for help queue `_ (password protected) - `Meeting recordings `_ (password protected) - `Homework solutions `_ (password protected) People ------ - Instructor + `Justin Bois `_ (`bois at caltech dot edu`) - TAs + Rosita Fu (`rfu at caltech dot edu`) + Matteo Guareschi (`mmguar at caltech dot edu`) + Arjuna Subramanian (`amsubram at caltech dot edu`) .. toctree:: :maxdepth: 1 :caption: Lessons lessons/00/setup.ipynb lessons/01/index lessons/02/plotting_posteriors.ipynb lessons/03/marginalization_by_numerical_quadrature.ipynb lessons/04/conjugacy.ipynb lesson_exercises/exercise_01.ipynb lessons/05/index lessons/06/index lesson_exercises/exercise_02.ipynb lessons/07/index lessons/08/index lessons/09/index lessons/10/mixture_model_stan.ipynb lessons/11/regression_with_stan.ipynb lesson_exercises/exercise_03.ipynb lessons/12/index lessons/13/prior_predictive_checks.ipynb lessons/14/posterior_predictive_checks.ipynb lesson_exercises/exercise_04.ipynb lessons/15/box_of_distributions.rst lessons/16/mcmc_diagnostics.ipynb lessons/17/funnel_of_hell.ipynb lesson_exercises/exercise_05.ipynb lessons/18/model_comparison.rst lessons/19/index lesson_exercises/exercise_06.ipynb lessons/20/index lessons/21/hierarchical_implementation.ipynb lesson_exercises/exercise_07.ipynb lessons/22/sbc.ipynb lessons/23/sbc_in_practice.ipynb lesson_exercises/exercise_08.ipynb lessons/24/intro_to_gps.ipynb lessons/25/index lesson_exercises/exercise_09.ipynb lessons/26/variational_inference.ipynb lessons/27/wrapup .. toctree:: :maxdepth: 1 :caption: Recitations recitations/01/probability_review.rst recitations/02/review_of_mle.ipynb recitations/03/choosing_priors.ipynb recitations/04/index recitations/05/index recitations/06/index recitations/07/index recitations/08/project_proposals.rst recitations/09/sampling_discrete_parameters.ipynb .. toctree:: :maxdepth: 1 :caption: Homework 0. Configuring your team homework/01/index homework/02/index homework/03/index homework/04/index homework/05/index homework/06/index homework/07/index homework/08/index homework/09/index homework/10/index 11. Course feedback .. toctree:: :maxdepth: 1 :caption: Schedule schedule .. toctree:: :maxdepth: 1 :caption: Policies policies .. toctree:: :maxdepth: 1 :caption: Resources resources Previous editions of the course ------------------------------- - `Winter 2021 `_ - `Winter 2020 `_