BE/Bi 103 b: Statistical Inference in the Biological Sciences
=============================================================

In the `prequel to this course <https://bebi103a.github.io/>`_, 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<Policies>` below. We will not go over them in detail in class, and it is your responsibility to understand them.


Useful links
-------------

- `Ed <https://edstem.org/us/courses/50280/>`_ (used for course communications)
- `Canvas <https://caltech.instructure.com/courses/6955/assignments>`_ (used for assignment submission/return)
- `Homework solutions <http://bebi103.caltech.edu/2024b_hw_solutions/hw_solutions.html>`_ (password protected)


People
------

- Instructor

   + `Justin Bois <http://bois.caltech.edu/>`_ (`bois at caltech dot edu`)

- TAs

   + Kayla Jackson
   + Zach Martinez
   + Kellan Moorse


.. toctree::
   :maxdepth: 1
   :caption: Lessons

   lessons/00/index
   lessons/01/index
   lessons/02/index
   lessons/03/plotting_posteriors.ipynb
   lessons/04/marginalization_by_numerical_quadrature.ipynb
   lessons/05/conjugacy.ipynb
   lesson_exercises/exercise_01.ipynb   
   lessons/06/index
   lesson_exercises/exercise_02.ipynb   
   lessons/07/index
   lessons/08/index
   lessons/09/mixture_model_stan.ipynb
   lessons/10/variate_covariate_with_stan.ipynb
   lesson_exercises/exercise_03.ipynb   
   lessons/11/index
   lessons/12/prior_predictive_checks.ipynb
   lessons/13/posterior_predictive_checks.ipynb
   lesson_exercises/exercise_04.ipynb   
   lessons/14/box_of_distributions.rst
   lessons/15/mcmc_diagnostics.ipynb
   lessons/16/funnel_of_hell.ipynb
   lesson_exercises/exercise_05.ipynb
   lessons/17/model_comparison.rst
   lessons/18/index
   lesson_exercises/exercise_06.ipynb
   lessons/19/index
   lessons/20/hierarchical_implementation.ipynb
   lesson_exercises/exercise_07.ipynb
   lessons/21/sbc.ipynb
   lessons/22/sbc_in_practice.ipynb
   lesson_exercises/exercise_08.ipynb
   lessons/23/intro_to_gps.ipynb
   lessons/24/index
   lessons/25/variational_inference.ipynb
   lesson_exercises/exercise_09.ipynb
   lessons/26/wrapup


.. toctree::
   :maxdepth: 1
   :caption: Recitations

   recitations/01/probability_review.rst
   recitations/02/choosing_priors.rst
   recitations/03/just_hw_help.rst
   recitations/04/index
   recitations/05/practice_model_building.ipynb
   recitations/06/HPC.ipynb
   recitations/07/sampling_discrete_parameters.ipynb
   recitations/08/project_proposals.rst
   recitations/09/just_hw_help.rst


.. toctree::
   :maxdepth: 1
   :caption: Homework

   0. Configuring your team <homework/00/hw0.1>
   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 <homework/11/hw11.ipynb>


.. toctree::
   :maxdepth: 1
   :caption: Schedule

   schedule


.. toctree::
   :maxdepth: 1
   :caption: Policies

   policies


.. toctree::
   :maxdepth: 1
   :caption: Resources

   resources


Previous editions of the course
------------------------------- 

- `Winter 2022 <http://bebi103.caltech.edu.s3-website-us-east-1.amazonaws.com/2022b/>`_
- `Winter 2021 <http://bebi103.caltech.edu.s3-website-us-east-1.amazonaws.com/2021b/>`_
- `Winter 2020 <http://bebi103.caltech.edu.s3-website-us-east-1.amazonaws.com/2020b/>`_