forked from avehtari/BDA_course_Aalto
-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
624 lines (535 loc) · 27.8 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<title>Bayesian Data Analysis course</title>
<script src="site_libs/jquery-1.11.3/jquery.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="site_libs/bootstrap-3.3.5/css/readable.min.css" rel="stylesheet" />
<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<style>h1 {font-size: 34px;}
h1.title {font-size: 38px;}
h2 {font-size: 30px;}
h3 {font-size: 24px;}
h4 {font-size: 18px;}
h5 {font-size: 16px;}
h6 {font-size: 12px;}
code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
pre:not([class]) { background-color: white }</style>
<script src="site_libs/jqueryui-1.11.4/jquery-ui.min.js"></script>
<link href="site_libs/tocify-1.9.1/jquery.tocify.css" rel="stylesheet" />
<script src="site_libs/tocify-1.9.1/jquery.tocify.js"></script>
<script src="site_libs/navigation-1.1/tabsets.js"></script>
<link href="site_libs/highlightjs-9.12.0/default.css" rel="stylesheet" />
<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>
<style type="text/css">code{white-space: pre;}</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
img {
max-width:100%;
}
.tabbed-pane {
padding-top: 12px;
}
.html-widget {
margin-bottom: 20px;
}
button.code-folding-btn:focus {
outline: none;
}
summary {
display: list-item;
}
pre code {
padding: 0;
}
</style>
<style type="text/css">
.dropdown-submenu {
position: relative;
}
.dropdown-submenu>.dropdown-menu {
top: 0;
left: 100%;
margin-top: -6px;
margin-left: -1px;
border-radius: 0 6px 6px 6px;
}
.dropdown-submenu:hover>.dropdown-menu {
display: block;
}
.dropdown-submenu>a:after {
display: block;
content: " ";
float: right;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
border-width: 5px 0 5px 5px;
border-left-color: #cccccc;
margin-top: 5px;
margin-right: -10px;
}
.dropdown-submenu:hover>a:after {
border-left-color: #adb5bd;
}
.dropdown-submenu.pull-left {
float: none;
}
.dropdown-submenu.pull-left>.dropdown-menu {
left: -100%;
margin-left: 10px;
border-radius: 6px 0 6px 6px;
}
</style>
<script type="text/javascript">
// manage active state of menu based on current page
$(document).ready(function () {
// active menu anchor
href = window.location.pathname
href = href.substr(href.lastIndexOf('/') + 1)
if (href === "")
href = "index.html";
var menuAnchor = $('a[href="' + href + '"]');
// mark it active
menuAnchor.tab('show');
// if it's got a parent navbar menu mark it active as well
menuAnchor.closest('li.dropdown').addClass('active');
// Navbar adjustments
var navHeight = $(".navbar").first().height() + 15;
var style = document.createElement('style');
var pt = "padding-top: " + navHeight + "px; ";
var mt = "margin-top: -" + navHeight + "px; ";
var css = "";
// offset scroll position for anchor links (for fixed navbar)
for (var i = 1; i <= 6; i++) {
css += ".section h" + i + "{ " + pt + mt + "}\n";
}
style.innerHTML = "body {" + pt + "padding-bottom: 40px; }\n" + css;
document.head.appendChild(style);
});
</script>
<!-- tabsets -->
<style type="text/css">
.tabset-dropdown > .nav-tabs {
display: inline-table;
max-height: 500px;
min-height: 44px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 4px;
}
.tabset-dropdown > .nav-tabs > li.active:before {
content: "";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
content: "";
border: none;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open:before {
content: "";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs > li.active {
display: block;
}
.tabset-dropdown > .nav-tabs > li > a,
.tabset-dropdown > .nav-tabs > li > a:focus,
.tabset-dropdown > .nav-tabs > li > a:hover {
border: none;
display: inline-block;
border-radius: 4px;
background-color: transparent;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
display: block;
float: none;
}
.tabset-dropdown > .nav-tabs > li {
display: none;
}
</style>
<!-- code folding -->
<style type="text/css">
#TOC {
margin: 25px 0px 20px 0px;
}
@media (max-width: 768px) {
#TOC {
position: relative;
width: 100%;
}
}
@media print {
.toc-content {
/* see https://github.com/w3c/csswg-drafts/issues/4434 */
float: right;
}
}
.toc-content {
padding-left: 30px;
padding-right: 40px;
}
div.main-container {
max-width: 1200px;
}
div.tocify {
width: 20%;
max-width: 260px;
max-height: 85%;
}
@media (min-width: 768px) and (max-width: 991px) {
div.tocify {
width: 25%;
}
}
@media (max-width: 767px) {
div.tocify {
width: 100%;
max-width: none;
}
}
.tocify ul, .tocify li {
line-height: 20px;
}
.tocify-subheader .tocify-item {
font-size: 0.90em;
}
.tocify .list-group-item {
border-radius: 0px;
}
</style>
</head>
<body>
<div class="container-fluid main-container">
<!-- setup 3col/9col grid for toc_float and main content -->
<div class="row">
<div class="col-xs-12 col-sm-4 col-md-3">
<div id="TOC" class="tocify">
</div>
</div>
<div class="toc-content col-xs-12 col-sm-8 col-md-9">
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="index.html">Bayesian Data Analysis course</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="index.html">Material</a>
</li>
<li>
<a href="Aalto2021.html">Aalto 2021</a>
</li>
<li>
<a href="assignments.html">Assignments</a>
</li>
<li>
<a href="project.html">Project</a>
</li>
<li>
<a href="demos.html">Demos</a>
</li>
<li>
<a href="FAQ.html">FAQ</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
</ul>
</div><!--/.nav-collapse -->
</div><!--/.container -->
</div><!--/.navbar -->
<div id="header">
<h1 class="title toc-ignore">Bayesian Data Analysis course</h1>
<h4 class="date">Page updated: 2021-08-30</h4>
</div>
<p>This is the web page for the Bayesian Data Analysis course at Aalto (CS-E5710) by <a href="https://users.aalto.fi/~ave/">Aki Vehtari</a>.</p>
<p>This web page will be updated during the August.</p>
<p>Aalto students should check also <a href="https://mycourses.aalto.fi/course/view.php?id=32457">MyCourses</a>. <strong>In 2021</strong> the course will be arranged completely online (pre-recorded lectures, live zoom QA sessions, course chat, online TA sessions, assignments and project submitted online, project presentation online). The registration for the course lectures will be used to estimate the need for the online resources. If you are unable to register for the course at the moment in the Sisu, there is no need to email the lecturer. You can start taking the course and register before the end of the course. Sisu shows rooms on campus for the computer exercises, but all the computer exercises and TA sessions are online. You can choose which TA session to join each week separately, without a need to register for those sessions.</p>
<ul>
<li><a href="https://into.aalto.fi/display/ensaannot/Aalto+University+Code+of+Academic+Integrity+and+Handling+Violations+Thereof">Aalto University Code of Academic Integrity and Handling Violations Thereof</a></li>
</ul>
<p>All the course material is available in a <a href="https://github.com/avehtari/BDA_course_Aalto">git repo</a> (and these pages are for easier navigation). All the material can be used in other courses. Text (except the BDA3 book) and videos licensed under CC-BY-NC 4.0. Code licensed under BSD-3.</p>
<div style="float:right;position: relative;">
<p><img src="bda_cover.png" /></p>
</div>
<p><a href="https://users.aalto.fi/~ave/BDA3.pdf">The electronic version of the course book Bayesian Data Analysis, 3rd ed, by by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin</a> is available for non-commercial purposes. Hard copies are available from <a href="https://www.crcpress.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955">the publisher</a> and many book stores. See also <a href="http://www.stat.columbia.edu/~gelman/book/">home page for the book</a>, <a href="http://www.stat.columbia.edu/~gelman/book/errata_bda3.txt">errata for the book</a>, and <a href="chapter_notes/BDA_notes.pdf">chapter notes</a>.</p>
<div id="prerequisites" class="section level2">
<h2>Prerequisites</h2>
<ul>
<li>Basic terms of probability theory
<ul>
<li>probability, probability density, distribution</li>
<li>sum, product rule, and Bayes’ rule</li>
<li>expectation, mean, variance, median</li>
<li>in Finnish, see e.g. <a href="http://math.aalto.fi/~lleskela/LectureNotes003.html">Stokastiikka ja tilastollinen ajattelu</a></li>
<li>in English, see e.g. Wikipedia and <a href="https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/readings/">Introduction to probability and statistics</a></li>
</ul></li>
<li>Some algebra and calculus</li>
<li>Basic visualisation techniques (R or Python)
<ul>
<li>histogram, density plot, scatter plot</li>
<li>see e.g. <a href="demos.html#BDA_R_demos">BDA R demos</a></li>
<li>see e.g. <a href="demos.html#BDA_Python_demos">BDA Python demos</a></li>
</ul></li>
</ul>
<p>This course has been designed so that there is strong emphasis in computational aspects of Bayesian data analysis and using the latest computational tools.</p>
<p>If you find BDA3 too difficult to start with, I recommend</p>
<ul>
<li>For regression models, their connection to statistical testing and causal analysis see <a href="https://avehtari.github.io/ROS-Examples/">Gelman, Hill and Vehtari, “Regression and Other Stories”</a>.</li>
<li>Richard McElreath’s <a href="https://xcelab.net/rm/statistical-rethinking/">Statistical Rethinking, 2nd ed</a> book is easier than BDA3 and the 2nd ed is excellent. Statistical Rethinking doesn’t go as deep in some details, math, algorithms and programming as BDA course. Richard’s lecture videos of <a href="https://github.com/rmcelreath/statrethinking_winter2019">Statistical Rethinking: A Bayesian Course Using R and Stan</a> are highly recommended even if you are following BDA3.</li>
<li>For background prerequisites some students have found chapters 2, 4 and 5 in <a href="https://sites.google.com/site/doingbayesiandataanalysis/">Kruschke, “Doing Bayesian Data Analysis”</a> useful.</li>
</ul>
</div>
<div id="course-contents-following-bda3" class="section level2">
<h2>Course contents following BDA3</h2>
<p>Bayesian Data Analysis, 3rd ed, by by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. <a href="http://www.stat.columbia.edu/~gelman/book/">Home page for the book</a>. <a href="http://www.stat.columbia.edu/~gelman/book/errata_bda3.txt">Errata for the book</a>. <a href="https://users.aalto.fi/~ave/BDA3.pdf">Electronic edition for non-commercial purposes only</a>.</p>
<ul>
<li>Background (Ch 1, Lecture 1)</li>
<li>Single-parameter models (Ch 2, Lecture 2)</li>
<li>Multiparameter models (Ch 3, Lecture 3)</li>
<li>Computational methods (Ch 10 , Lecture 4)</li>
<li>Markov chain Monte Carlo (Chs 11-12, Lectures 5-6)</li>
<li>Extra material for Stan and probabilistic programming (see below, Lecture 6)</li>
<li>Hierarchical models (Ch 5, Lecture 7)</li>
<li>Model checking (Ch 6, Lectures 8-9)
<ul>
<li>+ <a href="https://doi.org/10.1111/rssa.12378">Visualization in Bayesian workflow</a></li>
</ul></li>
<li>Evaluating and comparing models (Ch 7)
<ul>
<li>+ <a href="https://arxiv.org/abs/1507.04544">Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC</a> (<a href="https://doi.org/10.1007/s11222-016-9696-4">Journal link</a>)</li>
<li>+ <a href="https://avehtari.github.io/modelselection/">Videos and case studies</a></li>
<li>+ <a href="https://avehtari.github.io/modelselection/CV-FAQ.html">Cross-validation FAQ</a></li>
</ul></li>
<li>Decision analysis (Ch 9, Lecture 10)</li>
<li>Large sample properties and Laplace approximation (Ch 4, Lecture 11-12)</li>
<li>In addition you learn workflow for Bayesian data analysis</li>
</ul>
</div>
<div id="how-to-study" class="section level2">
<h2>How to study</h2>
<p>Recommended way to go through the material is</p>
<ul>
<li>Read the reading instructions for a chapter in <a href="chapter_notes/BDA_notes.pdf">chapter notes</a>.</li>
<li>Read the chapter in BDA3 and check that you find the terms listed in the reading instructions.</li>
<li>Watch the corresponding <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%22f0ec3a25-9e23-4935-873b-a9f401646812%22">lecture video</a> to get explanations for most important parts.</li>
<li>Read corresponding additional information in the <a href="chapter_notes/BDA_notes.pdf">chapter notes</a>.</li>
<li>Run the corresponding demos in <a href="https://github.com/avehtari/BDA_R_demos">R demos</a> or <a href="https://github.com/avehtari/BDA_py_demos">Python demos</a>.</li>
<li>Read the exercise instructions and make the corresponding <a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/assignments">assignments</a>. Demo codes in <a href="https://github.com/avehtari/BDA_R_demos">R demos</a> and <a href="https://github.com/avehtari/BDA_py_demos">Python demos</a> have a lot of useful examples for handling data and plotting figures. If you have problems, visit TA sessions or ask in course slack channel.</li>
<li>If you want to learn more, make also self study exercises listed below</li>
</ul>
</div>
<div id="slides-and-chapter-notes" class="section level2">
<h2>Slides and chapter notes</h2>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a>
<ul>
<li>including code for reproducing some of the figures</li>
</ul></li>
<li><a href="chapter_notes/BDA_notes.pdf">Chapter notes</a>
<ul>
<li>including reading instructions highlighting most important parts and terms</li>
</ul></li>
</ul>
</div>
<div id="videos" class="section level2">
<h2>Videos</h2>
<p>The following video motivates why computational probabilistic methods and probabilistic programming are important part of modern Bayesian data analysis.</p>
<ul>
<li><a href="https://www.youtube.com/watch?v=ukE5aqdoLZI">Computational probabilistic modeling in 15mins</a></li>
</ul>
<p>Short video clips on selected introductory topics are available in <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%22f0ec3a25-9e23-4935-873b-a9f401646812%22">a Panopto folder</a> and listed below.</p>
<ul>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=d841f429-9c3d-4d24-8228-a9f400efda7b">1.1 Introduction to uncertainty and modelling</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=13fc7889-cfd1-4d99-996c-a9f400f6e5a2">1.2 Introduction to the course contents</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=7a297f7d-bb7b-4dd0-9913-a9f500ec822d">2.1 Observation model, likelihood, posterior and binomial model</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=75b9f18f-e379-4557-a5fa-a9f500f11b40">2.2 Predictive distribution and benefit of integration</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=099659a5-f707-473d-8b03-a9f500f39eb5">2.3 Priors and prior information</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=f4b61f2a-4a94-43f7-828c-ac460144f64f">Extra 2 recorded 2020</a> with extra explanations about likelihood, normalization term, density, and conditioning on model M.</li>
</ul>
<p>The lecture videos are in <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%22f0ec3a25-9e23-4935-873b-a9f401646812%22">a Panopto folder</a> and listed below.</p>
<ul>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=9c271082-5a8c-4b66-b6c2-aacc00fc683f">Lecture 2.1</a> and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=70655a8a-0eb4-4ddd-9f52-aacc00fc67a2">Lecture 2.2</a> 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.
<ul>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=158d119d-8673-4120-8669-ac3900c13304">Lecture 2 extra</a> with extra explanations about likelihood, normalization term, density, and conditioning on model M.</li>
</ul></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ab958b4b-e2c4-4534-8305-aad100ba191f">Lecture 3</a> on multiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=8a3c7bbc-e2b8-4c16-97b2-aad800ba7927">Lecture 4.1</a> on numerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=44446861-eaa2-41b5-bf33-aad800caf18a">Lecture 4.2</a> on direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=098dfdb4-f3b8-46aa-b988-aadf00bd3177">Lecture 5.1</a> on Markov chain Monte Carlo, Gibbs sampling, Metropolis algorithm, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=9f657178-d8cf-4cb8-af62-aadf00cd9423">Lecture 5.2</a> on warm-up, convergence diagnostics, R-hat, and effective sample size. BDA3 Ch 11.</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=1744f6a0-84d3-4218-8a86-aae600ba7e84">Lecture 6.1</a> on HMC, NUTS, dynamic HMC and HMC specific convergence diagnostics, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e60ba1a9-f752-4b0a-88c6-aae600caa61a">Lecture 6.2</a> on probabilistic programming and Stan. BDA3 Ch 12 + <a href="#stan">extra material</a>.
<ul>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=10b958d4-9a26-41d5-9afe-ac2e00ee9b50">Stan Extra introduction</a> Golf putting example, main features of Stan, benefits of probabilistic programming, and comparison to some other software.</li>
</ul></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=79dee6de-afa9-446f-b533-aaf400cabf2b">Lecture 7.1</a> on hierarchical models, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c822561c-f95d-44fc-a1d0-aaf400d9fae3">Lecture 7.2</a> on exchangeability. BDA3 Ch 5.</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=2820df34-d958-4c6c-93f3-aaf400dece37">Project work info</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=7047e366-0df6-453c-867f-aafb00ca2d78">Lecture 8.1</a> on model checking, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=d7849131-0afd-4ae6-ad64-aafb00da36f4">Lecture 8.2</a> on cross-validation part 1. BDA3 Ch 6-7 + extra material.</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=50b2e73f-af0a-4715-b627-ab0200ca7bbd">Lecture 9.1</a> PSIS-LOO and K-fold-CV, <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=b0299d53-9454-4e33-9086-ab0200db14eeb">Lecture 9.2</a> model comparison and selection, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=4b6eeb48-ae64-4860-a8c3-ab0200e40ad8">Lecture 9.3</a> extra lecture on variable selection with projection predictive variable selection. Extra material.</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=82943720-de0f-4195-8639-ab0900ca2085">Lecture 10.1</a> on decision analysis. BDA3 Ch 9.</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=cad1e8f8-e1f0-408a-ad9d-ab0900db3977">Project presentation info</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e22fedc7-9fd3-4d1e-8318-ab1000ca45a4">Lecture 11.1</a> on normal approximation (Laplace approximation) and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=a8e38a95-a944-4f3d-bf95-ab1000dbdf73">Lecture 11.2</a> on large sample theory and counter examples. BDA3 Ch 4.</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e998b5dd-bf8e-42da-9f7c-ab1700ca2702">Lecture 12.1</a> on frequency evaluation, hypothesis testing and variable selection and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c43c862a-a5a4-45da-9b27-ab1700e12012">Lecture 12.2</a> overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21.</li>
</ul>
</div>
<div id="r-and-python" class="section level2">
<h2>R and Python</h2>
<p>We strongly recommend using R in the course as there are more packages for Stan and statistical analysis in R. If you are already fluent in Python, but not in R, then using Python may be easier, but it can still be more useful to learn also R. Unless you are already experienced and have figured out your preferred way to work with R, we recommend installing <a href="https://www.rstudio.com/products/rstudio/download/">RStudio Desktop</a> or using <a href="https://jupyter.cs.aalto.fi/">Aalto teaching JupyterHub</a>. See <a href="FAQ.html">FAQ</a> for frequently asked questions about R problems in this course. The <a href="demos.html">demo codes</a> provide useful starting points for all the assignments.</p>
<ul>
<li>For learning R programming basics we recommend
<ul>
<li><a href="https://rstudio-education.github.io/hopr/">Garrett Grolemund, Hands-On Programming with R</a></li>
</ul></li>
<li>For learning basic and advanced plotting using R we recommend
<ul>
<li><a href="https://socviz.co/">Kieran Healy, Data Visualization - A practical introduction</a></li>
<li><a href="http://www.gradaanwr.net/">Antony Unwin, Graphical Data Analysis with R</a></li>
</ul></li>
</ul>
</div>
<div id="demos" class="section level2">
<h2>Demos</h2>
<p>These demos include a lot of useful code for making the assignments.</p>
<ul>
<li><a href="demos.html#BDA_R_demos">R demos</a></li>
<li><a href="demos.html#BDA_Python_demos">Python demos</a></li>
</ul>
</div>
<div id="self-study-exercises" class="section level2">
<h2>Self study exercises</h2>
<p>Great self study BDA3 exercises for this course are listed below. Most of these have also <a href="http://www.stat.columbia.edu/~gelman/book/solutions3.pdf">model solutions available</a>.</p>
<ul>
<li>1.1-1.4, 1.6-1.8 (model solutions for 1.1-1.6)</li>
<li>2.1-2.5, 2.8, 2.9, 2.14, 2.17, 2.22 (model solutions for 2.1-2.5, 2.7-2.13, 2.16, 2.17, 2.20, and 2.14 is in slides)</li>
<li>3.2, 3.3, 3.9 (model solutions for 3.1-3.3, 3.5, 3.9, 3.10)</li>
<li>4.2, 4.4, 4.6 (model solutions for 3.2-3.4, 3.6, 3.7, 3.9, 3.10)</li>
<li>5.1, 5.2 (model solutions for 5.3-5.5, 5.7-5.12)</li>
<li>6.1 (model solutions for 6.1, 6.5-6.7)</li>
<li>9.1</li>
<li>10.1, 10.2 (model solution for 10.4)</li>
<li>11.1 (model solution for 11.1)</li>
</ul>
</div>
<div id="stan" class="section level2">
<h2>Stan</h2>
<ul>
<li><a href="http://mc-stan.org/">Stan home page</a></li>
<li><a href="http://www.stat.columbia.edu/~gelman/research/published/Stan-paper-aug-2015.pdf">Introductory article in Journal of Statistical Software</a></li>
<li><a href="http://mc-stan.org/documentation/">Documentation</a></li>
<li><a href="https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started">RStan installation</a></li>
<li><a href="https://pystan.readthedocs.io/en/latest/getting_started.html">PyStan installation</a></li>
<li>Basics of Bayesian inference and Stan, Jonah Gabry & Lauren Kennedy <a href="https://www.youtube.com/watch?v=ZRpo41l02KQ&t=8s&list=PLuwyh42iHquU4hUBQs20hkBsKSMrp6H0J&index=6">Part 1</a> and <a href="https://www.youtube.com/watch?v=6cc4N1vT8pk&t=0s&list=PLuwyh42iHquU4hUBQs20hkBsKSMrp6H0J&index=7">Part 2</a></li>
</ul>
</div>
<div id="extra-reading" class="section level2">
<h2>Extra reading</h2>
<ul>
<li><a href="https://doi.org/10.1111/j.1740-9713.2004.00050.x">Dicing with the unknown</a></li>
<li><a href="http://jeff560.tripod.com/b.html">Origin of word Bayesian</a></li>
<li><a href="https://avehtari.github.io/modelselection/">Model selection</a></li>
<li><a href="https://avehtari.github.io/modelselection/CV-FAQ.html">Cross-validation FAQ</a></li>
</ul>
</div>
<div id="acknowledgements" class="section level2">
<h2>Acknowledgements</h2>
<p>The course material has been greatly improved by the previous and current course assistants (in alphabetical order): Michael Riis Andersen, Paul Bürkner, Akash Dakar, Alejandro Catalina, Kunal Ghosh, Joona Karjalainen, Juho Kokkala, Måns Magnusson, Janne Ojanen, Topi Paananen, Markus Paasiniemi, Juho Piironen, Jaakko Riihimäki, Eero Siivola, Tuomas Sivula, Teemu Säilynoja, Jarno Vanhatalo.</p>
<p>The web page has been made with rmarkdown’s site generator.</p>
</div>
</div>
</div>
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
$(document).ready(function () {
$('.tabset-dropdown > .nav-tabs > li').click(function () {
$(this).parent().toggleClass('nav-tabs-open');
});
});
</script>
<!-- code folding -->
<script>
$(document).ready(function () {
// temporarily add toc-ignore selector to headers for the consistency with Pandoc
$('.unlisted.unnumbered').addClass('toc-ignore')
// move toc-ignore selectors from section div to header
$('div.section.toc-ignore')
.removeClass('toc-ignore')
.children('h1,h2,h3,h4,h5').addClass('toc-ignore');
// establish options
var options = {
selectors: "h1,h2,h3",
theme: "bootstrap3",
context: '.toc-content',
hashGenerator: function (text) {
return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
},
ignoreSelector: ".toc-ignore",
scrollTo: 0
};
options.showAndHide = true;
options.smoothScroll = true;
// tocify
var toc = $("#TOC").tocify(options).data("toc-tocify");
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>