-
Notifications
You must be signed in to change notification settings - Fork 36
/
Copy pathannotated.Rmd
495 lines (321 loc) · 29.4 KB
/
annotated.Rmd
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
# Notes
## Syllabi
- Ryan Bakker and Johannes Karreth, "Introduction to Applied Bayesian Modeling" ICPSR. Summer 2016.
- [Syllabus](http://www.jkarreth.net/files/bayes2016.pdf)
- [code](https://github.com/jkarreth/Bayes)
- Justin Esarey. "Advanced Topics in Political Methodology: Bayesian Statistics" Winter 2015.
- [Syllabus](http://jee3.web.rice.edu/POLS506-syllabus-2015.pdf)
- [Lectures](http://jee3.web.rice.edu/teaching.htm)
- Kruschke.
- [Doing Bayesian Data Analysis site](https://sites.google.com/site/doingbayesiandataanalysis/)
- Nick Beauchamp. "Bayesian Methods." NYU
- [syllabus](http://www.democraticwriting.com/work/Beauchamp_bayesian_syllabus.pdf)
- Alex Tanhk. "Bayesian Methods for the Social Sciences" U of Wisconsin. Spring 2017.
- [syllabus](https://polisci.wisc.edu/sites/polisci.wisc.edu/files/documents/syllabi/PS%20919%20.pdf)
- MTH225 Statistics for Science Spring 2016
- [github website](https://github.com/equinn1/MTH225_Spring2016)
- Ben Goodrich, "Bayesian Statistics for Social Sciences" Columbia University. Spring 2016.
- Bakker. "ntroduction to Applied Bayesian Analysis" University of Georgia.
- [syllabus](http://spia.uga.edu/faculty_pages/rbakker/bayes/bayes2016_maymester.pdf)
- [site](http://spia.uga.edu/faculty_pages/rbakker/bayes/POLS%20Bayes.htm)
- Myimoto. "Advances in Quantitative Psychology: Bayesian Statistics, Modeling & Reasoning" U of Washington. Winter 2017. [site](http://faculty.washington.edu/jmiyamot/p548/p548-set.htm)
- Kruschke. "Bayesian Data Analysis" Indiana University. Spring 2016.
- [PyMC code](https://github.com/aloctavodia/Doing_bayesian_data_analysis)
- Blackwell and Spirling. 2002. "Topics in Political Methodology" Harvard. Fall 2014. [Syllabus](http://www.mattblackwell.org/files/teaching/gov2002-syllabus.pdf). It has a couple of classes on Bayesian methods.
- Neil Frazer. Bayesian Data Analysis. Hawaii. Spring 2017. [syllabus](http://www.soest.hawaii.edu/GG/resources/syllabi-S17/gg695-s17-syl.pdf)
- Lopes. 2016. Bayesian Statistical Learning: Readings in Statistics and Econometrics. http://hedibert.org/current-teaching/
- Lopes. 2012 [Simulation-based approaches to modern Bayesian econometrics](http://hedibert.org/simulation-based-approaches-to-modern-bayesian-econometrics/). Short course.
- Lopes. 2015. Bayesian Econometrics. http://hedibert.org/current-teaching/
## Textbooks
- Gelman, Andrew, and Jennifer Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
- Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis. 3rd ed. CRC Press.
- Gelman, Andrew, Jessica Hwang, and Aki Vehtari. 2014. “Understanding Predictive Information Criteria for Bayesian Models.” Statistics and Computing 24 (6). Springer: 997–1016.
- Gill, Jeff. 2008. Bayesian Methods : A Social and Behavioral Sciences Approach. Second. Boca Raton: Chapman & Hall/CRC.
- Jackman, Simon. 2009. Bayesian Analysis for the Social Sciences. Chichester, UK: Wiley.
- Kruschke, John. 2010. Doing Bayesian Data Analysis: A Tutorial Introduction with R. Academic Press.
- Lynch, Scott M. 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer.
- McElreath, Richard. 2016. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Vol. 122. CRC Press.
- github page for Statistical Rethinking https://github.com/rmcelreath/rethinking
- http://xcelab.net/rm/statistical-rethinking/
- Lunn, David, Chris Jackson, Nicky Best, Andrew Thomas, and David Spiegelhalter. 2012. The BUGS Book: A Practical Introduction to Bayesian Analysis. Boca Raton, FL: Chapman; Hall/CRC.
- Suess, Eric A. and Bruce E. Trumbo. 2010. Introduction to Probability Simulation and
Gibbs Sampling with R. New York: Springer.
- Suess, Eric A. and Bruce E. Trumbo. 2010. Introduction to Probability Simulation and Gibbs Sampling with R. New York: Springer.
- Peter Hoff. 2009. A First Course in Bayesian Statistical Methods
- Jaynes. 2003. Probability Theory: The Logic of Science.
- Congdon. 2014. Applied Bayesian Modeling.
- Wakefield. 2013. Bayesian and Frequentist Regression Methods
- Casella and Roberts. 2004. Monte Carlo Statistical Methods
- Marin and Roberts. 2014. *Bayesian Essentials with R.* http://www.springer.com/us/book/9781461486862
## Topics
### Overviews
- Michael Clarke [Bayesian Basics](https://m-clark.github.io/docs/IntroBayes.html)
- Jackman. 2004. Bayesian Analysis for Political Research. *Annual Review of Political Science* DOI: 10.1146/annurev.polisci.7.012003.104706
- Kruschke, J.K. & Liddell, T.M. Psychon Bull Rev (2017). doi:10.3758/s13423-016-1221-4 - Cumming, G. (2014). The new statistics why and how. Psychological Science, 25(1), 7–29.
### Bayesian Philosophy
- Efron. 2010. The Future of Indirect Evidence. *Stat Sci* doi:10.1214/09-STS308
- Berger. 2006. The case for objective Bayesian analysis. *Bayesian Anal* doi:10.1214/06-BA115
- Brad Efron “Why Isn’t Everyone a Bayesian?” The American Statistician, Vol. 40, No. 1
(Feb., 1986) [include following discussion of Efron’s article]
- Chernoff. http://dx.doi.org/10.1080/00031305.1986.10475343
- Lindley. http://dx.doi.org/10.1080/00031305.1986.10475344
- Morris. http://dx.doi.org/10.1080/00031305.1986.10475345
- Press. http://dx.doi.org/10.1080/00031305.1986.10475346
- Smith. http://dx.doi.org/10.1080/00031305.1986.10475347
- Efron. Reply. http://dx.doi.org/10.1080/00031305.1986.10475348
- Philosophy and the practice of Bayesian statistics in the social sciences1. tp://www.stat.columbia.edu/~gelman/research/published/philosophy_chapter.pdf
- Aris Spanos "Revisiting data mining: ‘hunting’ with or without a license"
- Rubin (1984) Rubin, Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician. Ann. Statist. 12 (1984), no. 4, 1151--1172. doi:10.1214/aos/1176346785. http://projecteuclid.org/euclid.aos/1176346785.
- Andrew Gelman Induction and Deduction in Bayesian Data Analysis
- Berger, James O. Could Fisher, Jeffreys and Neyman Have Agreed on Testing?. Statist. Sci. 18 (2003), no. 1, 1--32. doi:10.1214/ss/1056397485. http://projecteuclid.org/euclid.ss/1056397485.
- Gross2014a: Gross, J. H. (2015), Testing What Matters (If You Must Test at All): A Context-Driven Approach to Substantive and Statistical Significance. American Journal of Political Science, 59: 775–788. doi:10.1111/ajps.12149
- Ng. and Jordan. On Discriminative vs. Generative classifiers: A Comparison of logistic regression and Naive Bayes: http://ai.stanford.edu/~ang/papers/nips01-discriminativegenerative.pdf
### Bayesian Frequentist Debates
- Casella and Berger. 1987. Reconciling Bayesian and Frequentist Evidence in the One-Sided Testing Problem. *JASA*. doi:10.1080/01621459.1987.10478396
- [Bayesians and Frequentists : Models, Assumptions, and Inference]( http://www.stat.ufl.edu/archived/casella/Talks/BayesRefresher.pdf) slides
- Kasss Statitsical Inference: The Big Picture https://arxiv.org/pdf/1106.2895v2.pdf
- Noah Smith [Bayesian vs. Frequentist: Is there any "there" there?](http://noahpinionblog.blogspot.com/2013/01/bayesian-vs-frequentist-is-there-any.html)
- Kass Kinds of Bayesians http://www.stat.cmu.edu/~kass/papers/kinds.pdf
- Anthony O'Hagan. Science, Subjectivity and Software (Comments on the articles by Berger and Goldstein)
- Good, I.J. (1971) 46656 varieties of Bayesians. Letter in American Statistician, 25: 62– 63. Reprinted in Good Thinking, University of Minnesota Press, 1982, pp. 20–21.
### Categorical
- Agresti. Bayesian Inference for Categorical Data Analysis. http://www.stat.ufl.edu/~aa/cda2/bayes.pdf
- **Perfect Separation**
- Gelman. 2008. "A weakly informative default prior distribution for logistic and other regression models" *Ann Applied Stat* doi:10.1214/08-AOAS191
- Rainey. 2016. "Dealing with Separation in Logistic Regression Models" *Political Analysis*a
- **Rare Events**
- King and Zheng. 2001. "Explaining Rare Events in International Relations" *Int Org* https://doi.org/10.1162/00208180152507597
- King, Gary, and Langche Zeng. 2001. "Logistic Regression in Rare Events Data." *Political Analysis* http://www.jstor.org/stable/25791637.
### Identifiability
- Weschler et al. 2013. A. Bayesian Look at Nonidentifiability: A Simple Example. *Am stat* http://dx.doi.org/10.1080/00031305.2013.778787
### Time Series
- Park, “Changepoint analysis of binary and ordinal probit models: An application to bank rate policy under the interwar gold standard”
### Topic Models
- Grimmer and Stewart, “Text as data: Te promise and pitfalls of automatic content analysis methods for
political texts”
- Quinn, Monroe, Colaresi, Crespin and Radev, “How to analyze political attention with minimal
assumptions and costs”
### Nonparametric Bayesian Methods
- Gill and Casella, “Nonparametric priors for ordinal Bayesian social science models”
- Spirling and Quinn, “Identifying intraparty voting blocs in the U.K. House of Commons”
### Prior Elicitation
- Gill, J. and Walker, L. D. (2005). Elicited Priors for Bayesian Model Specifications in Political Science Research. *Journal of Politics*
### Variable Selection
- Ghosh and Ghattas. 2015. Bayesian Variable Selection Under Collinearity. *Am Stat* http://dx.doi.org/10.1080/00031305.2015.1031827
### Shrinkage
- Efron, B. & Morris, C. 1975. "Data Analysis Using Stein's Estimator and its Generalizations" *JASA* doi:10.1080/01621459.1975.10479864
- https://baseballwithr.wordpress.com/2016/02/15/revisiting-efron-and-morriss-baseball-study/
### Applied Bayes Rule
Mostly examples of naive Bayes
## Computation Methods
#### Animations
- https://chi-feng.github.io/mcmc-demo/
- https://mimno.infosci.cornell.edu/hmc/; http://www.mimno.org/articles/hmc/
- http://twiecki.github.io/blog/2014/01/02/visualizing-mcmc/
- https://ridlow.wordpress.com/category/animation/
- http://people.math.aau.dk/~kkb/Undervisning/Bayes14/sorenh/docs/sampling-notes.pdf
- https://rpubs.com/mv2521/mcmc-animation
- http://blog.revolutionanalytics.com/2013/09/an-animated-peek-into-the-workings-of-bayesian-statistics.html
- https://people.duke.edu/~ccc14/sta-663/Animation.html
- https://artax.karlin.mff.cuni.cz/r-help/library/asbio/html/anm.mc.bvn.html
- https://groups.google.com/forum/#!topic/stan-users/nOk80xTlSyE
- https://www.youtube.com/watch?v=Vv3f0QNWvWQ
- https://theclevermachine.wordpress.com/2012/11/18/mcmc-hamiltonian-monte-carlo-a-k-a-hybrid-monte-carlo/
- https://www.youtube.com/watch?v=pHsuIaPbNbY&list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF&index=11
- http://arogozhnikov.github.io/2016/12/19/markov_chain_monte_carlo.html
#### Gibbs
- Gelfand et. al. 1986. "Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling" doi: 10.1080/01621459.1990.10474968
- Chib and Greenberg. "Understanding the Metropolis-Hastings Algorithm" doi:10.1080/00031305.1995.10476177
-
#### MCMC
- Casella Berger
- Jackman 2000
- Allison and Dunkley. 2013. Comparison of sampling techniques for Bayesian parameter estimation. https://arxiv.org/pdf/1308.2675.pdf
- https://courses.cs.washington.edu/courses/cse577/04sp/notes/dellaertUW.pdf
- Geyer. MCMC: Does it work? How can we tell?http://users.stat.umn.edu/~geyer/jsm09.pdf
#### HMCM
- Neal. 2011. MCMC using Hamiltonian dynamics. https://arxiv.org/pdf/1206.1901.pdf
- https://www.youtube.com/watch?v=xWQpEAyI5s8&index=12&list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF
- https://arxiv.org/pdf/1701.02434.pdf
- http://deeplearning.net/tutorial/hmc.html
#### SMC
- Liu and Chen. 1998. Sequential Monte Carlo Methods for Dynamic Systems. *JASA* 10.1080/01621459.1998.10473765
#### Variational
- Grimmer, “An Introduction to Bayesian Inference via Variational Approximations”
- Raganath et al. 2015. "Black Box Variational Inference" https://arxiv.org/abs/1401.0118
#### Expectation Propogation
- Gelman et. al. 2017. "Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data." https://arxiv.org/pdf/1412.4869.pdf
#### Importance Resampling
- Smith and Gelfand. 1992. "Bayesian Statistics without Tears: A Sampling–Resampling Perspective" *Am Stat* 10.1080/00031305.1992.10475856.
- Gelfand and Smith. "Sampling-Based Approaches to Calculating Marginal Densities" *JASA* 10.1080/01621459.1990.10476213
- Lopes, Hedibert F., Nicholas G. Polson, and Carlos M. Carvalho. "Bayesian Statistics with a Smile: A Resampling-sampling Perspective." *Brazilian Journal of Probability and Statistics* http://www.jstor.org/stable/43601224.
- [Simulation-based approaches to modern Bayesian
#### Approximate Bayesian
- Marin, Pudlo, Robert and Ryder, “Approximate Bayesian computational methods"
#### Author attribution
- Mosteller. 1964. Inference in an Authorship Problem. *JASA*
- Arefin, A. S.; Vimieiro, R.; Riveros, C.; Craig, H. & Moscato, P. Berwick, R. C. (Ed.) An Information Theoretic Clustering Approach for Unveiling Authorship Affinities in Shakespearean Era Plays and Poems PLoS ONE, Public Library of Science (PLoS), 2014. 10.1371/journal.pone.0111445. Not Bayesian per se, but has the corpus of Shakespeare and other plays.
### Software
Sofware for general purpose Bayesian computation is called [probablistic programming](https://en.wikipedia.org/wiki/Probabilistic_programming_language), though the term is used in CS and not so much in stats, or social science.
- [Stan](http://mc-stan.org/)
- Joseph Rickert. 2016. [R Stan and Statistics](https://www.r-bloggers.com/r-stan-and-bayesian-statistics/)
- BUGS modeling language. Models are specified in a different language.
- [NIMBLE](https://r-nimble.org/) A very new BUGS-like lanugage that works with R.
- [JAGS](http://mcmc-jags.sourceforge.net/) Gibbs/MCMC based
- [WinBUGS](https://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/) Gibbs and MCMC based software. It was
one of the first but is now obsolete and unmaintained. Use JAGS or Stan instead.
- [OpenBUGS](http://www.openbugs.net/w/FrontPage) The continuation of the WinBUGS project. Also no longer well maintained.
Use JAGS or Stan instead.
- R has multiple packages that implement some Bayesian methods. See the [Bayesian Task View](https://cran.r-project.org/web/views/Bayesian.html)
- [LearnBayes](https://cran.r-project.org/web/packages/LearnBayes/index.html)
- [TeachBayes](https://cran.r-project.org/web/packages/TeachBayes/index.html)
- Python
- [PyMC](https://pymc-devs.github.io/pymc3/) Very complete general-purpose Python package for Bayesian Analysis
- The various Machine learning packages like [SciKit]
- [Edward](https://github.com/blei-lab/edward). By David Blei. Deep generative models, variational inference. Runs
on Tensorflow. Implements variational and HMC methods, as well as optimization.
- Church and others. Lisp-based inference programs. These are from the CS side.
- Church
- [Anglican](http://www.robots.ox.ac.uk/~fwood/anglican/index.html)
-
- Stata: Since [Stata 14](http://www.stata.com/new-in-stata/bayesian-analysis/) it has some Bayesian capabilities. It
is mostly MH with Gibbs for a few models.
- Julia
- [Mamba](https://mambajl.readthedocs.io/en/latest/) MCMC supporting multiple methods including Gibbs, MH, HMC, slice
### Stan
Some R packages.
Official `stan-dev` packages:
- [rstan](https://cran.r-project.org/web/packages/rstan/index.html)
- [rstanarm](https://cran.r-project.org/web/packages/rstanarm/index.html)
- [bayesplot](https://cran.r-project.org/web/packages/bayesplot/index.html)
- [ShinyStan](https://cran.r-project.org/web/packages/shinystan/index.html)
- [loo](https://github.com/stan-dev/loo)
Others:
- [brms](https://github.com/paul-buerkner/brms) Bayesian generalized non-linear multilevel models using Stan
- [ggmcmc](https://cran.r-project.org/web/packages/ggmcmc/index.html)
### Diagrams
#### DAGs and Plate Notation
See [Plate notation](https://en.wikipedia.org/wiki/Plate_notation)
- [tikz-bayesnet](https://github.com/jluttine/tikz-bayesnet) A TiKZ library for drawing Bayesian networks
- [Daf](http://daft-pgm.org/) A python package to draw DAGs
- Relevant Stackoverflow questions:
- [Software for drawing bayesian networks (graphical models)] (http://stats.stackexchange.com/questions/16750/software-for-drawing-bayesian-networks-graphical-models) Stackoverflow.
- [Tikz Example](http://www.texample.net/tikz/examples/bayes/)
- [how to draw plate indices in graphical model by tikz](http://tex.stackexchange.com/questions/199734/how-to-draw-plate-indices-in-graphical-model-by-tikz) Stackexchange
- [Can I have automatically adjusted plates in a graphical model?](http://tex.stackexchange.com/questions/11751/can-i-have-automatically-adjusted-plates-in-a-graphical-model?rq=1)
#### Kruschke Diagrams
Diagrams in the style of Kruschke's *Doing Bayesian Analysis*
- LibreOffice Draw Templates: http://www.sumsar.net/blog/2013/10/diy-kruschke-style-diagrams/
- Blog posts
- http://doingbayesiandataanalysis.blogspot.se/2012/05/graphical-model-diagrams-in-doing.html
- http://doingbayesiandataanalysis.blogspot.se/2012/05/hierarchical-diagrams-read-bottom-to.html
- http://doingbayesiandataanalysis.blogspot.se/2013/10/diagrams-for-hierarchical-models-we.html
- R scripts: https://github.com/rasmusab/distribution_diagrams
- Tikz scripts: https://github.com/yozw/bayesdiagram
#### Venn Diagrams/Eikosograms
- Oldford and W.H. Cherry. 2006. "Picturing Probability: the poverty of Venn diagrams, the richness of Eikosograms"
### Political Science Bayesian Works
- Darmofal2009a: Darmofal, D. (2009), Bayesian Spatial Survival Models for Political Event Processes. American Journal of Political Science, 53: 241–257. doi:10.1111/j.1540-5907.2008.00368.x
- RosasShomerHaptonstahl2014a: Rosas, G., Shomer, Y. and Haptonstahl, S. R. (2015), No News Is News: Nonignorable Nonresponse in Roll-Call Data Analysis. American Journal of Political Science, 59: 511–528. doi:10.1111/ajps.12148
- Joseph Bafumi, Andrew Gelman, David K. Park, Noah Kaplan; Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation. Polit Anal 2005; 13 (2): 171-187. doi: 10.1093/pan/mpi010
- Arthur Spirling; Bayesian Approaches for Limited Dependent Variable Change Point Problems. Polit Anal 2007; 15 (4): 387-405. doi: 10.1093/pan/mpm022
- Kari Lock, Andrew Gelman; Bayesian Combination of State Polls and Election Forecasts. Polit Anal 2010; 18 (3): 337-348. doi: 10.1093/pan/mpq002
- Jacob M. Montgomery, Brendan Nyhan; Bayesian Model Averaging: Theoretical Developments and Practical Applications. Polit Anal 2010; 18 (2): 245-270. doi: 10.1093/pan/mpq001
- Kevin M. Quinn; Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses. Polit Anal 2004; 12 (4): 338-353. doi: 10.1093/pan/mph022
- Ryan Bakker, Keith T. Poole; Bayesian Metric Multidimensional Scaling. Polit Anal 2013; 21 (1): 125-140. doi: 10.1093/pan/mps039
- Clinton Joshua D, Jackman Simon D, Rivers Douglas. The statistical analysis of roll call data: A unified approach, American Political Science Review , 2004, vol. 98 (pg. 355-70)
- Pope Jeremy C, Treier Shawn A. Reconsidering the great compromise at the federal convention of 1787: Deliberation and agenda effects on the senate and slavery, American Journal of Political Science , 2011, vol. 55 (pg. 289-306)
- Martin Andrew D, Quinn Kevin M. Dynamic ideal point estimation via Markov chain Monte Carlo for the U.S. Supreme Court, 1953–1999, Political Analysis , 2002, vol. 10 (pg. 134-53)
- Justin Grimmer; A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases. Polit Anal 2010; 18 (1): 1-35. doi: 10.1093/pan/mpp034
- Jacob M. Montgomery, Florian M. Hollenbach, Michael D. Ward; Improving Predictions Using Ensemble Bayesian Model Averaging. Polit Anal 2012; 20 (3): 271-291. doi: 10.1093/pan/mps002
- Stegmueller2013a: Stegmueller, D. (2013), How Many Countries for Multilevel Modeling? A Comparison of Frequentist and Bayesian Approaches. American Journal of Political Science, 57: 748–761. doi:10.1111/ajps.12001
- HareArmstrongBakkerEtAl2014a: Hare, C., Armstrong, D. A., Bakker, R., Carroll, R. and Poole, K. T. (2015), Using Bayesian Aldrich-McKelvey Scaling to Study Citizens' Ideological Preferences and Perceptions. American Journal of Political Science, 59: 759–774. doi:10.1111/ajps.12151
- HonakerKing2010a: Honaker, J. and King, G. (2010), What to Do about Missing Values in Time-Series Cross-Section Data. American Journal of Political Science, 54: 561–581. doi:10.1111/j.1540-5907.2010.00447.x
- ImaiTingley2011a: Imai, K. and Tingley, D. (2012), A Statistical Method for Empirical Testing of Competing Theories. American Journal of Political Science, 56: 218–236. doi:10.1111/j.1540-5907.2011.00555.x
- Park2010aL Hee Park, J. (2010), Structural Change in U.S. Presidents' Use of Force. American Journal of Political Science, 54: 766–782. doi:10.1111/j.1540-5907.2010.00459.x
- 10.1111/j.1540-5907.2012.00590.x
- Park2012a: Park, J. H. (2012), A Unified Method for Dynamic and Cross-Sectional Heterogeneity: Introducing Hidden Markov Panel Models. American Journal of Political Science, 56: 1040–1054. doi:10.1111/j.1540-
- WawroKatznelson2013a: Wawro, G. J. and Katznelson, I. (2014), Designing Historical Social Scientific Inquiry: How Parameter Heterogeneity Can Bridge the Methodological Divide between Quantitative and Qualitative Approaches. American Journal of Political Science, 58: 526–546. doi:10.1111/ajps.12041
- Western, B., & Jackman, S. (1994). Bayesian Inference for Comparative Research. <i>American Political Science Review,</i> <i>88</i>(2), 412-423. doi:10.2307/2944713
## Model Checking
- Gelman, Andrew, and Iain Pardoe. 2006. “Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models.” Technometrics 48 (2). Taylor & Francis: 241–51.
- Gelman, Andrew. A Bayesian Formulation of Exploratory Data Analysis and Goodness-of-fit Testing. Internat. Statist. Rev. 71 (2003), no. 2, 369--382. http://projecteuclid.org/euclid.isr/1069172304.
- Kruschke, J. K. (2011). Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science, 6(3) 299–312.
- Andrew Gelman Jessica Hwang and Aki Vehtari. 2013. Understanding predictive information criteria for Bayesian models. http://www.stat.columbia.edu/~gelman/research/published/waic_understand3.pdf
- Vehtari, Gelman, and Gabry. Practical Bayesian model evaluation using leave-one-out cross-validation and
WAIC. 2016. http://www.stat.columbia.edu/~gelman/research/unpublished/loo_stan.pdf
WAID.
- LOO package in R: https://github.com/stan-dev/loo
- Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely application information criterion in singular learning theory. Journal of Machine Learning Research 11, 3571-3594.
- Gelfand, A. E. (1996). Model determination using sampling-based methods. In Markov Chain Monte Carlo in Practice, ed. W. R. Gilks, S. Richardson, D. J. Spiegelhalter, 145-162. London: Chapman and Hall.
- Gelfand, A. E., Dey, D. K., and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods. In Bayesian Statistics 4, ed. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, 147-167. Oxford University Press.
- Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing 24, 997-1016.
- Vehtari and Lampinen. 2002. Bayesian model assessment and comparison using cross-validation predictive densities. https://doi.org/10.1162/08997660260293292
- Vehtari and Ojanen. 2012. A survey of Bayesian predictive methods for model assessment, selection and comparison. doi:10.1214/12-SS102
- Cook, Gelman, and Rubin. 2006. Validation of Software for Bayesian Models Using Posterior Quantiles. *J of Comp. and Graphical Stat* DOI:10.1198/106186006X136976
## General Applications and Models
### Mixed Methods and Qualitative Research
- Macartan Humphreys and Alan M. Jacobs, 2015, “Mixing Methods: A Bayesian Approach”, *American Political Science Review*
## Hierarchical Modeling
- Kruschke and Vanpaeml "Bayesian Estimation in Hierarchical Models" http://www.indiana.edu/~kruschke/articles/KruschkeVanpaemel2015.pdf
- David K. Park, Andrew Gelman, Joseph Bafumi. 2004. "Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls." *Polit Anal* doi:10.1093/pan/mph024
- Lax, Jeffrey and Justin Phillips. 2009. "How Should We Estimate Public Opinion in the States?" *AJPS*
## Shrinkage/Regularization
- Piironen and Vehtari. 2016. On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. https://arxiv.org/abs/1610.05559
- Lopes. 2015. [Bayesian Regularization](http://hedibert.org/wp-content/uploads/2015/12/BayesianRegularization.pdf) slides.
### Examples
- Monroe, B. L.; Colaresi, M. P. & Quinn™, K. M. Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict Political Analysis, Cambridge University Press (CUP), 2008, https://doi.org/10.1093/pan/mpn018
- Beauchamp. 2016. Predicting and Interpolating State-Level Polls Using Twitter Textual Data: Juho Piironen, Aki Vehtari. Projection predictive model selection for Gaussian processes. https://arxiv.org/abs/1510.04813
- forecasting and predictiing civil war (Fearon / Laitin)
Goldstone et al. 2009. "A Global Model for Forecasting Political Instability" *AJPS* 10.1111/j.1540-5907.2009.00426.x
- Ward et al. 2017/ Lessons from near real-time forecasting of irregular leadership changes.
*JPR* http://dx.doi.org/10.1177%2F0022343316680858
- Andy Berger. Coup forecasts for 2017. http://andybeger.com/2017/02/10/coup-forecasts-2017/
- http://imai.princeton.edu/research/files/afghan.pdf
### Latent Variable Models
- CLINTON, J., JACKMAN, S., & RIVERS, D. (2004). The Statistical Analysis of Roll Call Data. <i>American Political Science Review,</i> <i>98</i>(2), 355-370. doi:10.1017/S0003055404001194
- Pope, J. C. and Treier, S. (2011), Reconsidering the Great Compromise at the Federal Convention of 1787: Deliberation and Agenda Effects on the Senate and Slavery. American Journal of Political Science, 55: 289–306. doi:10.1111/j.1540-5907.2010.00490.x
- Cai et al. 2016. Item Response Theory. *Ann rev of Stat and Its Application* DOI: 10.1146/annurev-statistics-041715-033702
## Bayes Theorem Examples
### Miscallaneous
- Monty Hall Problem: http://marilynvossavant.com/game-show-problem/
- Examples from Kahnehman
- Fenton, Neil, and Berger. 2016. "Bayes and the Law" *Ann Rev of Stat and Its Application* DOI: 10.1146/annurev-statistics-041715-033428
- Taddy. 2013. Multinomial Inverse Regression for Text Analysis. *JASA* http://dx.doi.org/10.1080/01621459.2012.734168
- Taddy. 2015. Document Classification by Inversion of Distributed Language Representations.
- Laver et al. 2003. Extracting Policy Positions from Political Texts Using Words as Data. Laver, Michael, Kenneth Benoit, and John Garry. "Extracting Policy Positions from Political Texts Using Words as Data." The American Political Science Review. http://www.jstor.org/stable/3118211.
### German Tank Problem
- https://en.wikipedia.org/wiki/German_tank_problem
- Goodman 1954. Some Practical Techniques in Serial Number Analysis. *JASA* doi:10.1080/01621459.1954.10501218
- Johnson. 1994. Estimating the Size of a Population. *Teaching Stats* DOI:10.1111/j.1467-9639.1994.tb00688.x
- Ruggles and Brodie. 1947. An Empirical Approach to Economic Intelligence in World War II. doi:10.1080/01621459.1947.10501915
Other applications
- Gill and Spirling. 2015. Estimating the Severity of the WikiLeaks U.S. Diplomatic Cables Disclosure. https://doi.org/10.1093/pan/mpv005. *Political Analysis* doi:10.1093/pan/mpv005
## Good-Turing Estimator
- Mosteller. 1964. Inference in an Authorship Problem. *JASA*
## Reproducibility
- Jon Zelner: [Docker package of an R and Stan project](http://www.jonzelner.net/statistics/make/docker/reproducibility/2016/05/31/reproducibility-pt-1/)
- https://github.com/kjhealy/lintscreen
- https://msalganik.wordpress.com/2015/06/09/rapid-feedback-on-code-with-lintr/
- https://msalganik.wordpress.com/2015/06/07/git-and-github-in-a-data-analysis-class/
- http://astrofrog.github.io/blog/2013/04/10/how-to-conduct-a-full-code-review-on-github/
- http://www.princeton.edu/~mjs3/soc504_s2015/submitting_homework.shtml
- https://education.github.com/guide
- https://msalganik.wordpress.com/2015/05/04/replication-and-extension-projects-making-class-more-interesting-and-useful/
- https://en.wikipedia.org/wiki/Good%E2%80%93Turing_frequency_estimation
- https://dx.doi.org/10.1093%2Fbiomet%2F40.3-4.237
- http://rstudio-pubs-static.s3.amazonaws.com/165358_78fd356d6e124331bd66981c51f7ad7c.html
- https://www.cs.cornell.edu/courses/cs6740/2010sp/guides/lec11.pdf
- https://www.rdocumentation.org/packages/edgeR/versions/3.14.0/topics/goodTuring
- http://kochanski.org/gpk/teaching/0401Oxford/GoodTuring.pdf
- http://www.cs.dartmouth.edu/~lorenzo/teaching/cs134/Archive/Spring2010/milestone/20100511-134-milestone-cooley/node5.html
- https://simons.berkeley.edu/events/openlectures2015-spring-1
- http://www.grsampson.net/D_SGT.c
- https://courses.engr.illinois.edu/cs498jh/Slides/Lecture03HO.pdf
- http://ic.epfl.ch/files/content/sites/ic/files/Inka/Orlitsky%20Talk%202016.pdf
### Uncategorized
- Travelling Politician Example: https://github.com/ctufts/metropolis_hastings_example/tree/master/claydavis
## Empirical Bayes
- Efron. 2015. Frequentist accuracy of Bayesian estimates. *JRSS B* https://dx.doi.org/10.1111%2Frssb.12080
## Things to cover
- Lindley's paradox