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Schedule overview

3. Multidimensional posterior BDA3 Chapter 3 -2023 Lecture 3.1,
2023 Lecture 3.2
Slides 3 +2024 Lecture 3.1,
2024 Lecture 3.2 Assignment 3 2024-09-23 2024-09-29 @@ -454,7 +454,7 @@

3) BD
  • Lecture Monday 2024-09-23. 14:15-16, hall T1, CS building
  • Read the additional comments for Chapter 3
  • Check R demos or Python demos for Chapter 3
    • diff --git a/assignments/template3_files/figure-html/unnamed-chunk-10-2.png b/assignments/template3_files/figure-html/unnamed-chunk-10-2.png index 099eda1d..844c5629 100644 Binary files a/assignments/template3_files/figure-html/unnamed-chunk-10-2.png and b/assignments/template3_files/figure-html/unnamed-chunk-10-2.png differ diff --git a/search.json b/search.json index ca37c8a9..30e4d30a 100644 --- a/search.json +++ b/search.json @@ -326,7 +326,7 @@ "href": "Aalto2024.html#schedule-2024", "title": "Bayesian Data Analysis course - Aalto 2024", "section": "Schedule 2024", - "text": "Schedule 2024\nThe course consists of 12 lectures, 9 assignments, a project work, and a project presentation in periods I and II. It’s good start reading the material for the next lecture and assignment while making the assignment related to the previous lecture. There are 9 assignments and a project work with presentation, and thus the assignments are not in one-to-one correspondence with the lectures. The schedule below lists the lectures and how they connect to the topics, book chapters and assignments.\n\nSchedule overview\nHere is an overview of the schedule. Scroll down the page to see detailed instructions for each block. When you are working on assignment related to previous lecture, it is good to start reading the book chapters relaed to the next lecture and assignment. The schedule links to 2023 lecture videos until couple hours after the 2024 lecture has been recorded.\n\n\n\n\nReadings\nLectures\nAssignment\nLecture Date\nAssignment due date\n\n\n\n\n1. Introduction\nBDA3 Chapter 1\n2024 Lecture 1.1 Introduction, 2024 Lecture 1.2 Course practicalities, Slides 1.1, Slides 1.2\n2024 Assignment 1\n2024-09-02\n2024-09-15\n\n\n2. Basics of Bayesian inference\nBDA3 Chapter 1, BDA3 Chapter 2\n2023 Lecture 2.1 (2024 recording failed), 2024 Lecture 2.2, Slides 2\nAssignment 2\n2024-09-16\n2024-09-22\n\n\n3. Multidimensional posterior\nBDA3 Chapter 3\n2023 Lecture 3.1, 2023 Lecture 3.2Slides 3\nAssignment 3\n2024-09-23\n2024-09-29\n\n\n4. Monte Carlo\nBDA3 Chapter 10\n2023 Lecture 4.1, 2023 Lecture 4.2, Slides 4\nOld Assignment 4\n2024-09-30\n2024-10-06\n\n\n5. Markov chain Monte Carlo\nBDA3 Chapter 11\n2023 Lecture 5.1, 2023 Lecture 5.2, Slides 5\nOld Assignment 5\n2024-10-07\n2024-10-13\n\n\n6. Stan, HMC, PPL\nBDA3 Chapter 12 + extra material on Stan\n2023 Lecture 6.1, 2023 Lecture 6.2, Slides 6\nOld Assignment 6\n2024-10-14\n2024-10-27\n\n\n7. Hierarchical models and exchangeability\nBDA3 Chapter 5\n2023 Lecture 7.1, 2023 Lecture 7.2, 2022 Project info, Slides 7\nOld Assignment 7\n2024-10-28\n2024-11-03\n\n\n8. Model checking & cross-validation\nBDA3 Chapter 6, BDA3 Chapter 7, Visualization in Bayesian workflow, Practical Bayesian cross-validation\n2023 Lecture 8.1, 2023 Lecture 8.2, Slides 8a,Slides 8b\nStart project work\n2024-11-04\nN/A\n\n\n9. Model comparison, selection, and hypothesis testing\nBDA3 Chapter 7 (not 7.2 and 7.3), Practical Bayesian cross-validation\n2023 Lecture 9.1, 2023 Lecture 9.2, Slides 9\nOld Assignment 8\n2024-11-11\n2024-11-10\n\n\n10. Decision analysis\nBDA3 Chapter 9\n2023 Lecture 10.1, 2023 Lecture 10.2, Slides 10a, Slides 10b\nOld Assignment 9\n2024-11-18\n2024-11-17\n\n\n11. Variable selectio with projpred, project presentation example\nBDA3 Chapter 4\n2023 Lecture 11.1, 2023 Lecture 11.2, 2023 Lecture 11.3, Slides 11a, Slides Project Presentation, Slides 11 extra\nProject work\n2024-11-25\nN/A\n\n\n12. TBA\n\nOptional: \nProject work\n2024-12-02\nN/A\n\n\n13. Project evaluation\n\n\n\nProject presentations: 9.-13.12.\nEvaluation week\n\n\n\n\n\n1) Course introduction, BDA 3 Ch 1, prerequisites assignment\nCourse practicalities, material, assignments, project work, peergrading, QA sessions, TA sessions, prerequisites, chat, etc.\n\nLogin with Aalto account to the Zulip course chat with link in MyCourses\nIntroduction/practicalities lecture Monday 2024-09-02 14:15-16, hall C, Otakaari 1**\n\n2024 Lecture videos 1.1 and 1.2 in Panopto\nSlides 1.1, Slides 1.2\n\nRead BDA3 Chapter 1\n\nstart with reading instructions for Chapter 1 and afterwards read the additional comments in the same document\n\nThere are no R demos for Chapter 1\nMake and submit 2024 Assignment 1. Deadline Sunday 2024-09-15 23:59\n\nWe highly recommend to submit all assignments Friday before 3pm so that you can get TA help before submission. As the course has students who work weekdays (e.g. FiTech students), the late submission until Sunday night is allowed, but we can’t provide support during the weekends.\nthis assignment checks that you have sufficient prerequisite skills (basic probability calculus, and R or Python)\nGeneral information about assignments\n\nR markdown template for assignments\nFAQ for the assignments has solutions to commonly asked questions related RStudio setup, errors during package installations, etc.\n\n\nGet help in TA sessions 2024-09-04 14-16, Y342a, Otakaari 1 2024-09-05 12-14, Y429c-d, Otakaari 1\n\nin Sisu these are marked as exercise sessions, but we call them TA sessions\nthese are optional and you can choose which one to join\nsee more info about TA sessions\n\nHighly recommended, but optional: Make BDA3 exercises 1.1-1.4, 1.6-1.8 (model solutions available for 1.1-1.6)\nStart reading Chapters 1+2, see instructions below\n\n\n\n2) BDA3 Ch 1+2, basics of Bayesian inference\nBDA3 Chapters 1+2, basics of Bayesian inference, observation model, likelihood, posterior and binomial model, predictive distribution and benefit of integration, priors and prior information, and one parameter normal model.\n\nRead BDA3 Chapter 2\n\nsee reading instructions for Chapter 2\n\nLecture Monday 2024-09-16 14:15-16, hall T1, CS building\n\nSlides 2\nVideos: 2023 Lecture 2.1 (2024 recording failed), 2024 Lecture 2.2 on basics of Bayesian inference, observation model, likelihood, posterior and binomial model, predictive distribution and benefit of integration, priors and prior information, and one parameter normal model. BDA3 Ch 1+2.\n\nRead the additional comments for Chapter 2\nCheck R demos or Python demos for Chapter 2\nMake and submit Assignment 2. Deadline Sunday 2024-09-22 23:59\n\nTA sessions 2024-09-18 14-16, Y342a, Otakaari 1 2024-09-19 12-14, Y429c-d, Otakaari 1\n\nHighly recommended, but optional: Make BDA3 exercises 2.1-2.5, 2.8, 2.9, 2.14, 2.17, 2.22 (model solutions available for 2.1-2.5, 2.7-2.13, 2.16, 2.17, 2.20, and 2.14 is in course slides)\nStart reading Chapter 3, see instructions below\n\n\n\n3) BDA3 Ch 3, multidimensional posterior\nMultiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.\n\nRead BDA3 Chapter 3\n\nsee reading instructions for Chapter 3\n\nLecture Monday 2024-09-23. 14:15-16, hall T1, CS building\n\nSlides 3\nVideos: 2023 Lecture 3.1 2023 Lecture 3.2 on multiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.\n\nRead the additional comments for Chapter 3\nCheck R demos or Python demos for Chapter 3\nMake and submit Assignment 3. Deadline Sunday 2024-09-29 23:59\nTA sessions 2024-09-25 14-16, 2024-09-26 12-14,\nHighly recommended, but optional: Make BDA3 exercises 3.2, 3.3, 3.9 (model solutions available for 3.1-3.3, 3.5, 3.9, 3.10)\nStart reading Chapter 10, see instructions below\n\n\n\n4) BDA3 Ch 10, Monte Carlo\nNumerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.\n\nRead BDA3 Chapter 10\n\nsee reading instructions for Chapter 10\n\nLecture Monday 2024-09-30 14:15-16, hall T1, CS building\n\nSlides 4\nVideos: 2023 Lecture 4.1 on numerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, and 2023 Lecture 4.2 on direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.\n\nRead the additional comments for Chapter 10\nCheck R demos or Python demos for Chapter 10\nMake and submit Old Assignment 4. Deadline Sunday 2024-10-06 23:59\nTA sessions 2024-10-02 14-16, 2024-10-03 12-14,\nHighly recommended, but optional: Make BDA3 exercises 10.1, 10.2 (model solution available for 10.4)\nStart reading Chapter 11, see instructions below\n\n\n\n5) BDA3 Ch 11, Markov chain Monte Carlo\nMarkov chain Monte Carlo, Gibbs sampling, Metropolis algorithm, warm-up, convergence diagnostics, R-hat, and effective sample size. BDA3 Ch 11.\n\nRead BDA3 Chapter 11\n\nsee reading instructions for Chapter 11\n\nLecture Monday 2024-10-07 14:15-16, hall T1, CS building\n\nSlides 5\nVideos: 2023 Lecture 5.1 on Markov chain Monte Carlo, Gibbs sampling, Metropolis algorithm, and 2023 Lecture 5.2 on warm-up, convergence diagnostics, R-hat, and effective sample size.\n\nRead the additional comments for Chapter 11\nCheck R demos or Python demos for Chapter 11\nMake and submit Old Assignment 5. Deadline Sunday 2024-10-13 23:59\nTA sessions 2024-10-09 14-16, 2024-10-10 12-14,\nHighly recommended, but optional: Make BDA3 exercise 11.1 (model solution available for 11.1)\nStart reading Chapter 12 + Stan material, see instructions below\n\n\n\n6) BDA3 Ch 12 + Stan, HMC, PPL, Stan\nHMC, NUTS, dynamic HMC and HMC specific convergence diagnostics, probabilistic programming and Stan. BDA3 Ch 12 + extra material\n\nRead BDA3 Chapter 12\n\nsee reading instructions for Chapter 12\n\nLecture Monday 2024-10-14 14:15-16, hall T1, CS building\n\nSlides 6\nVideos: 2022 Lecture 6.1 on HMC, NUTS, dynamic HMC and HMC specific convergence diagnostics, and 2022 Lecture 6.2 on probabilistic programming and Stan. BDA3 Ch 12 + extra material.\nOptional: Stan Extra introduction recorded 2020 Golf putting example, main features of Stan, benefits of probabilistic programming, and comparison to some other software.\n\nRead the additional comments for Chapter 12\nRead Stan introduction article\nCheck R demos for RStan or Python demos for PyStan\nAdditional material for Stan:\n\nDocumentation\nRStan installation\nPyStan installation\nBasics of Bayesian inference and Stan, Jonah Gabry & Lauren Kennedy Part 1 and Part 2\n\nMake and submit Old Assignment 6. DeadlineSunday 2024-10-27 23:59 (two weeks for this assignment)\nTA sessions 2024-10-16 14-16, 2024-10-17 12-14,\nStart reading Chapter 5 + Stan material, see instructions below\n\n\n\n7) BDA3 Ch 5, hierarchical models\nHierarchical models and exchangeability. BDA3 Ch 5.\n\nRead BDA3 Chapter 5\n\nsee reading instructions for Chapter 5\n\nLecture Monday 2024-10-28 14:15-16, hall T2, CS building\n\nSlides 7\nVideos: 2023 Lecture 7.1 on hierarchical models, 2023 Lecture 7.2 on exchangeability.\n\nRead the additional comments for Chapter 5\nCheck R demos or Python demos for Chapter 5\nMake and submit Old Assignment 7. Deadline Sunday 2024-11-03 23:59 (two weeks for this assignment)\nTA sessions 2024-10-30 14-16, 2024-10-31 12-14,\nHighly recommended, but optional: Make BDA3 exercises 5.1 and 5.2 (model solution available for 5.3-5.5, 5.7-5.12)\nStart reading Chapters 6-7 and additional material, see instructions below.\n\n\n\n8) BDA3 Ch 6+7 + extra material, model checking, cross-validation\nModel checking and cross-validation.\n\nRead BDA3 Chapters 6 and 7 (skip 7.2 and 7.3)\n\nsee reading instructions for Chapter 6 and Chapter 7\n\nRead Visualization in Bayesian workflow\n\nmore about workflow and examples of prior predictive checking and LOO-CV probability integral transformations\n\nRead Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (Journal link)\n\nreplaces BDA3 Sections 7.2 and 7.3 on cross-validation\n\nLecture Monday 2024-11-04 14:15-16, hall T2, CS building\n\nSlides 8a, Slides 8b\nVideos: 2022 Lecture 8.1 on model checking, and 2023 Lecture 8.2 on cross-validation part 1. BDA3 Ch 6-7 + extra material.\n\nRead the additional comments for Chapter 6 and Chapter 7\nCheck R demos or Python demos for Chapter 6\nAdditional reading material\n\nCross-validation FAQ\n\nNo new assignment in this block\nStart the project work\nTA sessions 2024-11-06 14-16, 2024-11-07 12-14,\nHighly recommended, but optional: Make BDA3 exercise 6.1 (model solution available for 6.1, 6.5-6.7)\n\n\n\n9) BDA3 Ch 7, extra material, model comparison and selection\nPSIS-LOO, K-fold-CV, model comparison and selection. Extra lecture on variable selection with projection predictive variable selection.\n\nRead Chapter 7 (no 7.2 and 7.3)\n\nsee reading instructions for Chapter 7\n\nRead Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (Journal link)\n\nreplaces BDA3 Sections 7.2 and 7.3 on cross-validation\n\nLecture Monday 2024-11-11 14:15-16, hall T2, CS building\n\nSlides 9\nVideos: 2023 Lecture 9.1 and 2023 Lecture 9.2 on model comparison, selection, and hypothesis testing.\n\nAdditional reading material\n\nCV FAQ\n\nMake and submit Old Assignment 8. Sunday 2024-11-10 23:59\nTA sessions 2024-11-13 14-16, 2024-11-14 12-14,\nStart reading Chapter 9, see instructions below.\n\n\n\n10) BDA3 Ch 9, decision analysis + BDA3 Ch 4 Laplace approximation and asymptotics\nDecision analysis. BDA3 Ch 9. + Laplace approximation and asymptotics. BDA Ch 4.\n\nRead Chapter 9 and 4\n\nsee reading instructions for Chapter 9\nsee reading instructions for Chapter 4\n\nLecture Monday 2024-11-18 14:15-16, hall T2, CS building\n\nSlides 10a, Slides 10b\nVideos: 2023 Lecture 10.1 on decision analysis. BDA3 Ch 9, and 2023 Lecture 10.2 on Laplace approximation, and asymptotics, BDA3 Ch 4.\n\nMake and submit Old Assignment 9. Sunday 2024-11-17 23:59\nTA sessions 2024-11-20 14-16, 2024-11-21 12-14,\nStart reading Chapter 4, see instructions below.\n\n\n\n11) Variable selection with projpred, project presentation example, extra\n\nLecture Monday 2024-11-25 14:15-16, hall T2, CS building\n\nSlides 11a, Slides Project Presentation, Slides 11 extra\nVideos: 2023 Lecture 11.1 on variable selecion with projpred, 2023 Lecture 11.2 on project presentations, 2023 Lecture 11.3 on rest of BDA3, ROS, and Bayesian Workflow\n\nNo new assignment. Work on project. TAs help with projects.\nTA sessions 2024-11-27 14-16, 2024-11-28 12-14,\n\n\n\n12) TBA\n\nLecture Monday 2024-12-02 14:15-16, hall T2, CS building\n\nSlides 12\n\nTBA\nWork on project. TAs help with projects. Project deadline 1.12. 23:59\nTA sessions 2024-12-04 14-16, 2024-12-05 12-14,\n\n\n\n13) Project evaluation\n\nProject report deadline 1.12. 23:59 (submit to peergrade).\n\nReview project reports done by your peers before 6.12. 23:59, and reflect on your feedback.\n\nProject presentations 9.-13.12. (evaluation week)" + "text": "Schedule 2024\nThe course consists of 12 lectures, 9 assignments, a project work, and a project presentation in periods I and II. It’s good start reading the material for the next lecture and assignment while making the assignment related to the previous lecture. There are 9 assignments and a project work with presentation, and thus the assignments are not in one-to-one correspondence with the lectures. The schedule below lists the lectures and how they connect to the topics, book chapters and assignments.\n\nSchedule overview\nHere is an overview of the schedule. Scroll down the page to see detailed instructions for each block. When you are working on assignment related to previous lecture, it is good to start reading the book chapters relaed to the next lecture and assignment. The schedule links to 2023 lecture videos until couple hours after the 2024 lecture has been recorded.\n\n\n\n\nReadings\nLectures\nAssignment\nLecture Date\nAssignment due date\n\n\n\n\n1. Introduction\nBDA3 Chapter 1\n2024 Lecture 1.1 Introduction, 2024 Lecture 1.2 Course practicalities, Slides 1.1, Slides 1.2\n2024 Assignment 1\n2024-09-02\n2024-09-15\n\n\n2. Basics of Bayesian inference\nBDA3 Chapter 1, BDA3 Chapter 2\n2023 Lecture 2.1 (2024 recording failed), 2024 Lecture 2.2, Slides 2\nAssignment 2\n2024-09-16\n2024-09-22\n\n\n3. Multidimensional posterior\nBDA3 Chapter 3\n2024 Lecture 3.1, 2024 Lecture 3.2\nAssignment 3\n2024-09-23\n2024-09-29\n\n\n4. Monte Carlo\nBDA3 Chapter 10\n2023 Lecture 4.1, 2023 Lecture 4.2, Slides 4\nOld Assignment 4\n2024-09-30\n2024-10-06\n\n\n5. Markov chain Monte Carlo\nBDA3 Chapter 11\n2023 Lecture 5.1, 2023 Lecture 5.2, Slides 5\nOld Assignment 5\n2024-10-07\n2024-10-13\n\n\n6. Stan, HMC, PPL\nBDA3 Chapter 12 + extra material on Stan\n2023 Lecture 6.1, 2023 Lecture 6.2, Slides 6\nOld Assignment 6\n2024-10-14\n2024-10-27\n\n\n7. Hierarchical models and exchangeability\nBDA3 Chapter 5\n2023 Lecture 7.1, 2023 Lecture 7.2, 2022 Project info, Slides 7\nOld Assignment 7\n2024-10-28\n2024-11-03\n\n\n8. Model checking & cross-validation\nBDA3 Chapter 6, BDA3 Chapter 7, Visualization in Bayesian workflow, Practical Bayesian cross-validation\n2023 Lecture 8.1, 2023 Lecture 8.2, Slides 8a,Slides 8b\nStart project work\n2024-11-04\nN/A\n\n\n9. Model comparison, selection, and hypothesis testing\nBDA3 Chapter 7 (not 7.2 and 7.3), Practical Bayesian cross-validation\n2023 Lecture 9.1, 2023 Lecture 9.2, Slides 9\nOld Assignment 8\n2024-11-11\n2024-11-10\n\n\n10. Decision analysis\nBDA3 Chapter 9\n2023 Lecture 10.1, 2023 Lecture 10.2, Slides 10a, Slides 10b\nOld Assignment 9\n2024-11-18\n2024-11-17\n\n\n11. Variable selectio with projpred, project presentation example\nBDA3 Chapter 4\n2023 Lecture 11.1, 2023 Lecture 11.2, 2023 Lecture 11.3, Slides 11a, Slides Project Presentation, Slides 11 extra\nProject work\n2024-11-25\nN/A\n\n\n12. TBA\n\nOptional: \nProject work\n2024-12-02\nN/A\n\n\n13. Project evaluation\n\n\n\nProject presentations: 9.-13.12.\nEvaluation week\n\n\n\n\n\n1) Course introduction, BDA 3 Ch 1, prerequisites assignment\nCourse practicalities, material, assignments, project work, peergrading, QA sessions, TA sessions, prerequisites, chat, etc.\n\nLogin with Aalto account to the Zulip course chat with link in MyCourses\nIntroduction/practicalities lecture Monday 2024-09-02 14:15-16, hall C, Otakaari 1**\n\n2024 Lecture videos 1.1 and 1.2 in Panopto\nSlides 1.1, Slides 1.2\n\nRead BDA3 Chapter 1\n\nstart with reading instructions for Chapter 1 and afterwards read the additional comments in the same document\n\nThere are no R demos for Chapter 1\nMake and submit 2024 Assignment 1. Deadline Sunday 2024-09-15 23:59\n\nWe highly recommend to submit all assignments Friday before 3pm so that you can get TA help before submission. As the course has students who work weekdays (e.g. FiTech students), the late submission until Sunday night is allowed, but we can’t provide support during the weekends.\nthis assignment checks that you have sufficient prerequisite skills (basic probability calculus, and R or Python)\nGeneral information about assignments\n\nR markdown template for assignments\nFAQ for the assignments has solutions to commonly asked questions related RStudio setup, errors during package installations, etc.\n\n\nGet help in TA sessions 2024-09-04 14-16, Y342a, Otakaari 1 2024-09-05 12-14, Y429c-d, Otakaari 1\n\nin Sisu these are marked as exercise sessions, but we call them TA sessions\nthese are optional and you can choose which one to join\nsee more info about TA sessions\n\nHighly recommended, but optional: Make BDA3 exercises 1.1-1.4, 1.6-1.8 (model solutions available for 1.1-1.6)\nStart reading Chapters 1+2, see instructions below\n\n\n\n2) BDA3 Ch 1+2, basics of Bayesian inference\nBDA3 Chapters 1+2, basics of Bayesian inference, observation model, likelihood, posterior and binomial model, predictive distribution and benefit of integration, priors and prior information, and one parameter normal model.\n\nRead BDA3 Chapter 2\n\nsee reading instructions for Chapter 2\n\nLecture Monday 2024-09-16 14:15-16, hall T1, CS building\n\nSlides 2\nVideos: 2023 Lecture 2.1 (2024 recording failed), 2024 Lecture 2.2 on basics of Bayesian inference, observation model, likelihood, posterior and binomial model, predictive distribution and benefit of integration, priors and prior information, and one parameter normal model. BDA3 Ch 1+2.\n\nRead the additional comments for Chapter 2\nCheck R demos or Python demos for Chapter 2\nMake and submit Assignment 2. Deadline Sunday 2024-09-22 23:59\n\nTA sessions 2024-09-18 14-16, Y342a, Otakaari 1 2024-09-19 12-14, Y429c-d, Otakaari 1\n\nHighly recommended, but optional: Make BDA3 exercises 2.1-2.5, 2.8, 2.9, 2.14, 2.17, 2.22 (model solutions available for 2.1-2.5, 2.7-2.13, 2.16, 2.17, 2.20, and 2.14 is in course slides)\nStart reading Chapter 3, see instructions below\n\n\n\n3) BDA3 Ch 3, multidimensional posterior\nMultiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.\n\nRead BDA3 Chapter 3\n\nsee reading instructions for Chapter 3\n\nLecture Monday 2024-09-23. 14:15-16, hall T1, CS building\n\nSlides 3\nVideos: 2024 Lecture 3.1 2024 Lecture 3.2 on multiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.\n\nRead the additional comments for Chapter 3\nCheck R demos or Python demos for Chapter 3\nMake and submit Assignment 3. Deadline Sunday 2024-09-29 23:59\nTA sessions 2024-09-25 14-16, 2024-09-26 12-14,\nHighly recommended, but optional: Make BDA3 exercises 3.2, 3.3, 3.9 (model solutions available for 3.1-3.3, 3.5, 3.9, 3.10)\nStart reading Chapter 10, see instructions below\n\n\n\n4) BDA3 Ch 10, Monte Carlo\nNumerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.\n\nRead BDA3 Chapter 10\n\nsee reading instructions for Chapter 10\n\nLecture Monday 2024-09-30 14:15-16, hall T1, CS building\n\nSlides 4\nVideos: 2023 Lecture 4.1 on numerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, and 2023 Lecture 4.2 on direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.\n\nRead the additional comments for Chapter 10\nCheck R demos or Python demos for Chapter 10\nMake and submit Old Assignment 4. Deadline Sunday 2024-10-06 23:59\nTA sessions 2024-10-02 14-16, 2024-10-03 12-14,\nHighly recommended, but optional: Make BDA3 exercises 10.1, 10.2 (model solution available for 10.4)\nStart reading Chapter 11, see instructions below\n\n\n\n5) BDA3 Ch 11, Markov chain Monte Carlo\nMarkov chain Monte Carlo, Gibbs sampling, Metropolis algorithm, warm-up, convergence diagnostics, R-hat, and effective sample size. BDA3 Ch 11.\n\nRead BDA3 Chapter 11\n\nsee reading instructions for Chapter 11\n\nLecture Monday 2024-10-07 14:15-16, hall T1, CS building\n\nSlides 5\nVideos: 2023 Lecture 5.1 on Markov chain Monte Carlo, Gibbs sampling, Metropolis algorithm, and 2023 Lecture 5.2 on warm-up, convergence diagnostics, R-hat, and effective sample size.\n\nRead the additional comments for Chapter 11\nCheck R demos or Python demos for Chapter 11\nMake and submit Old Assignment 5. Deadline Sunday 2024-10-13 23:59\nTA sessions 2024-10-09 14-16, 2024-10-10 12-14,\nHighly recommended, but optional: Make BDA3 exercise 11.1 (model solution available for 11.1)\nStart reading Chapter 12 + Stan material, see instructions below\n\n\n\n6) BDA3 Ch 12 + Stan, HMC, PPL, Stan\nHMC, NUTS, dynamic HMC and HMC specific convergence diagnostics, probabilistic programming and Stan. BDA3 Ch 12 + extra material\n\nRead BDA3 Chapter 12\n\nsee reading instructions for Chapter 12\n\nLecture Monday 2024-10-14 14:15-16, hall T1, CS building\n\nSlides 6\nVideos: 2022 Lecture 6.1 on HMC, NUTS, dynamic HMC and HMC specific convergence diagnostics, and 2022 Lecture 6.2 on probabilistic programming and Stan. BDA3 Ch 12 + extra material.\nOptional: Stan Extra introduction recorded 2020 Golf putting example, main features of Stan, benefits of probabilistic programming, and comparison to some other software.\n\nRead the additional comments for Chapter 12\nRead Stan introduction article\nCheck R demos for RStan or Python demos for PyStan\nAdditional material for Stan:\n\nDocumentation\nRStan installation\nPyStan installation\nBasics of Bayesian inference and Stan, Jonah Gabry & Lauren Kennedy Part 1 and Part 2\n\nMake and submit Old Assignment 6. DeadlineSunday 2024-10-27 23:59 (two weeks for this assignment)\nTA sessions 2024-10-16 14-16, 2024-10-17 12-14,\nStart reading Chapter 5 + Stan material, see instructions below\n\n\n\n7) BDA3 Ch 5, hierarchical models\nHierarchical models and exchangeability. BDA3 Ch 5.\n\nRead BDA3 Chapter 5\n\nsee reading instructions for Chapter 5\n\nLecture Monday 2024-10-28 14:15-16, hall T2, CS building\n\nSlides 7\nVideos: 2023 Lecture 7.1 on hierarchical models, 2023 Lecture 7.2 on exchangeability.\n\nRead the additional comments for Chapter 5\nCheck R demos or Python demos for Chapter 5\nMake and submit Old Assignment 7. Deadline Sunday 2024-11-03 23:59 (two weeks for this assignment)\nTA sessions 2024-10-30 14-16, 2024-10-31 12-14,\nHighly recommended, but optional: Make BDA3 exercises 5.1 and 5.2 (model solution available for 5.3-5.5, 5.7-5.12)\nStart reading Chapters 6-7 and additional material, see instructions below.\n\n\n\n8) BDA3 Ch 6+7 + extra material, model checking, cross-validation\nModel checking and cross-validation.\n\nRead BDA3 Chapters 6 and 7 (skip 7.2 and 7.3)\n\nsee reading instructions for Chapter 6 and Chapter 7\n\nRead Visualization in Bayesian workflow\n\nmore about workflow and examples of prior predictive checking and LOO-CV probability integral transformations\n\nRead Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (Journal link)\n\nreplaces BDA3 Sections 7.2 and 7.3 on cross-validation\n\nLecture Monday 2024-11-04 14:15-16, hall T2, CS building\n\nSlides 8a, Slides 8b\nVideos: 2022 Lecture 8.1 on model checking, and 2023 Lecture 8.2 on cross-validation part 1. BDA3 Ch 6-7 + extra material.\n\nRead the additional comments for Chapter 6 and Chapter 7\nCheck R demos or Python demos for Chapter 6\nAdditional reading material\n\nCross-validation FAQ\n\nNo new assignment in this block\nStart the project work\nTA sessions 2024-11-06 14-16, 2024-11-07 12-14,\nHighly recommended, but optional: Make BDA3 exercise 6.1 (model solution available for 6.1, 6.5-6.7)\n\n\n\n9) BDA3 Ch 7, extra material, model comparison and selection\nPSIS-LOO, K-fold-CV, model comparison and selection. Extra lecture on variable selection with projection predictive variable selection.\n\nRead Chapter 7 (no 7.2 and 7.3)\n\nsee reading instructions for Chapter 7\n\nRead Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (Journal link)\n\nreplaces BDA3 Sections 7.2 and 7.3 on cross-validation\n\nLecture Monday 2024-11-11 14:15-16, hall T2, CS building\n\nSlides 9\nVideos: 2023 Lecture 9.1 and 2023 Lecture 9.2 on model comparison, selection, and hypothesis testing.\n\nAdditional reading material\n\nCV FAQ\n\nMake and submit Old Assignment 8. Sunday 2024-11-10 23:59\nTA sessions 2024-11-13 14-16, 2024-11-14 12-14,\nStart reading Chapter 9, see instructions below.\n\n\n\n10) BDA3 Ch 9, decision analysis + BDA3 Ch 4 Laplace approximation and asymptotics\nDecision analysis. BDA3 Ch 9. + Laplace approximation and asymptotics. BDA Ch 4.\n\nRead Chapter 9 and 4\n\nsee reading instructions for Chapter 9\nsee reading instructions for Chapter 4\n\nLecture Monday 2024-11-18 14:15-16, hall T2, CS building\n\nSlides 10a, Slides 10b\nVideos: 2023 Lecture 10.1 on decision analysis. BDA3 Ch 9, and 2023 Lecture 10.2 on Laplace approximation, and asymptotics, BDA3 Ch 4.\n\nMake and submit Old Assignment 9. Sunday 2024-11-17 23:59\nTA sessions 2024-11-20 14-16, 2024-11-21 12-14,\nStart reading Chapter 4, see instructions below.\n\n\n\n11) Variable selection with projpred, project presentation example, extra\n\nLecture Monday 2024-11-25 14:15-16, hall T2, CS building\n\nSlides 11a, Slides Project Presentation, Slides 11 extra\nVideos: 2023 Lecture 11.1 on variable selecion with projpred, 2023 Lecture 11.2 on project presentations, 2023 Lecture 11.3 on rest of BDA3, ROS, and Bayesian Workflow\n\nNo new assignment. Work on project. TAs help with projects.\nTA sessions 2024-11-27 14-16, 2024-11-28 12-14,\n\n\n\n12) TBA\n\nLecture Monday 2024-12-02 14:15-16, hall T2, CS building\n\nSlides 12\n\nTBA\nWork on project. TAs help with projects. Project deadline 1.12. 23:59\nTA sessions 2024-12-04 14-16, 2024-12-05 12-14,\n\n\n\n13) Project evaluation\n\nProject report deadline 1.12. 23:59 (submit to peergrade).\n\nReview project reports done by your peers before 6.12. 23:59, and reflect on your feedback.\n\nProject presentations 9.-13.12. 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