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![Page 1: Bayesian Statistics, Modeling & Reasoning What is this course about? P548: Intro Bayesian Stats with Psych Applications Instructor: John Miyamoto 01/04/2016:](https://reader036.fdocuments.us/reader036/viewer/2022062311/5a4d1b007f8b9ab059986597/html5/thumbnails/1.jpg)
Bayesian Statistics, Modeling & ReasoningWhat is this course about?
P548: Intro Bayesian Stats with Psych ApplicationsInstructor: John Miyamoto
01/04/2016: Lecture 01-1
Note: This Powerpoint presentation may contain macros that I wrote to help me create the slides. The macros aren’t needed to view the slides. You can disable or delete the macros without any change to the presentation.
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Outline
• What is Bayesian inference?
• Why are Bayesian statistics, modeling & reasoning relevant to psychology?
• What is Psych 548 about?
• Explain Psych 548 website
• Intro to R
• Intro to RStudio
• Intro to the R to BUGS interface
Psych 548, Miyamoto, Win '16 2
Lecture probably ends here
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3
Bayes Rule – What Is It?
• Reverend Thomas Bayes, 1702 – 1761English Protestant minister & mathematician
• Bayes Rule is fundamentally important to:♦ Bayesian statistics♦ Bayesian decision theory♦ Bayesian models in psychology
Psych 548, Miyamoto, Win '16
P Data|Hypothesis P(Hypothesis)P(Hypothesis|Data)
P(Data)=
n
i ii 1
P(Data) P Data | Hypothesis P Hypothesis
Bayes Rule – Why Is It Important?
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4Psych 548, Miyamoto, Win '16
Bayes Rule – Why Is It Important?
• Bayes Rule is the optimal way to update the probability of hypotheses given data.
• The concept of "Bayesian reasoning“: 3 related conceptso Concept 1: Bayesian inference is a model of optimal learning
from experience.o Concept 2: Bayesian decision theory describes optimal strategies
for taking actions in an uncertain environment. Optimal gambling strategies.
o Concept 3: Bayesian reasoning represents the uncertainty of events as probabilities in a mathematical calculus.
• Concepts 1, 2 & 3 are all consistent with the use of the term, "Bayesian", in modern psychology.
Bayesian Issues in Psychology
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5Psych 548, Miyamoto, Win '16
Bayesian Issues in Psychological Research
• Does human reasoning about uncertainty conform to Bayes Rule? Do humans reason about uncertainty as if they are manipulating probabilities?
o These questions are posed with respect to infants & children,as well as adults.
• Do neural information processing systems (NIPS) incorporate Bayes Rule?
• Do NIPS model uncertainties as if they are probabilities.
Four Roles for Bayesian Reasoning in Psychology Research
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6Psych 548, Miyamoto, Win '16
Four Roles for Bayesian Reasoning in Psychology
1. Bayesian statistics: Analyzing datao E.g., is the slope of the regression of grades on IQ the same for boys as
for girls?o E.g., are there group differences in an analysis of variance?
Four Roles …. (Continued)
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7Psych 548, Miyamoto, Win '16
Four Roles for Bayesian Reasoning in Psychology
1. Bayesian statistics: Analyzing data
2. Bayesian decision theory – a theory of strategic action.How to gamble if you must.
3. Bayesian modeling of psychological processes
4. Bayesian reasoning – Do people reason as if they are Bayesian probability analysts? (At macro & neural levels)
o Judgment and decision making – This is a major issue.o Human causal reasoning – is it Bayesian or quasi-Bayesian?o Modeling neural decision making – many proposed models have a
strong Bayesian flavor.
Four Roles …. (Continued)
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8Psych 548, Miyamoto, Win '16
Four Roles for Bayesian Reasoning in Psychology
1. Bayesian statistics: Analyzing data
2. Bayesian decision theory – a theory of strategic action.How to gamble if you must.
3. Bayesian modeling of psychological processes
4. Bayesian reasoning – Do people reason as if they are Bayesian probability analysts? (At macro & neural levels)
Graphical Representation of Psych 548 Focus on Stats/Modeling
Psych 548: Focus on Topics (1) and (3). Include a little bit of (4).
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9Psych 548, Miyamoto, Win '16
Graphical Representation of Psych 548
Bayesian Statistics& Modeling:
R & JAGS
Bayesian Models in Cognitive Psychology
& Neuroscience
Psych 548
Graph & Text Showing the History of S, S-Plus & R
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10Psych 548, Miyamoto, Win '16
Brief History of S, S-Plus, & R
• S – open source statistics program created by Bell Labs (1976 – 1988 – 1999)
• S-Plus – commercial statistics program, refinement of S (1988 – present)
• R – free open source statistics program (1997 – present)
o currently the standard computing framework for statisticians worldwideMany contributors to its development
o Excellent general computation. Powerful & flexible.
o Great graphics.o Multiplatform: Unix, Linux, Windows, Maco User must like programming
BUGS, WinBUGS, OpenBUGS, JAGS
S
S-PlusR
Ancestry of R
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11Psych 548, Miyamoto, Win '16
BUGS, WinBUGS, OpenBUGS & JAGS
• Gibbs Sampling & Metropolis-Hastings AlgorithmTwo algorithms for sampling from a hard-to-evaluate probability distribution.
• BUGS – Bayesian inference Under Gibbs Sampling (circa 1995)
• WinBUGS - Open source (circa 1997)o Windows only
• OpenBUGS – Open source (circa 2006) o Mainly Windows. Runs within a virtual Windows machine on a Mac.
• JAGS – Open source (circa 2007)o Multiplatform: Windows, Mac, Linux
• STAN – Open source (circa 2012) Multiplatform: Windows, Mac, Linux
Basic Structure of Bayesian Computation with R & OpenBUGS
“BUGS” includes all of these.
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12Psych 548, Miyamoto, Win '16
Basic Structure of Bayesian Computation
R
data preparation
analysis of results
JAGSComputes
approximation to the posterior distribution.Includes diagnostics.
rjags functions
rjags functions
rjagsrunjags
OpenBUGS/WinBUGS/
StanR BRugs functions
Brugs functions
BRugsR2WinBUGS
rstan
Outline of Remainder of the Lecture: Course Outline & General Information
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RStudio
• Run RStudio
• Run R from within RStudio
Psych 548, Miyamoto, Win '16 13
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14Psych 548, Miyamoto, Win '16
Remainder of This Lecture
• Take 5 minute break
• Introduce selves
• Psych 548: What will we study?
• Briefly view the Psych 548 webpage.
• Introduction to the computer facility in CSSCR.
• Introduction to R, BUGS (OpenBUGS & JAGS), and RStudio
5 Minute Break
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5 Minute Break
• Introduce selves upon return
Psych 548, Miyamoto, Win '16 15Course Goals
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16Psych 548, Miyamoto, Win '16
Course Goals
• Learn the theoretical framework of Bayesian inference.
• Achieve competence with R, OpenBUGS and JAGS.
• Learn basic Bayesian statisticso Learn how to think about statistical inference from a Bayesian standpoint. o Learn how to interpret the results of a Bayesian analysis. o Learn basic tools of Bayesian statistical inference - testing for
convergence, making standard plots, examing samples from a posterior distribution.
---------------------------------------------------------------
Secondary Goalso Bayesian modeling in psychologyo Understand arguments about Bayesian reasoning in the psychology
of reasoning. The pros and cons of the heuristics & biases movement.
Kruschke Textbook
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Kruschke, Doing Bayesian Data Analysis
Kruschke, J. K. (2014). Doing bayesian data analysis, second edition: A tutorial with R, JAGS, and Stan. Academic Press.
• Excellent textbook – worth the price ($90 from Amazon)
• Emphasis on classical statistical test problems from a Bayesian perspective. Not so much modeling per se.
♦ Binomial inference problems, anova problems, linear regression problems.
Computational Requirements
• R & JAGS (or OpenBUGS)
• A programming editor like Rstudio is useful.
Psych 548, Miyamoto, Win '16 17Chapter Outline of Kruschke Textbook
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Kruschke, Doing Bayesian Data Analysis
• Ch 1 – 4: Basic probability background (pretty easy)
• Ch 5 – 8: Bayesian inference with simple binomial models♦ Conjugate priors, Gibbs sampling & Metropolis-Hastings algorithm♦ OpenBUGS or JAGS
• Ch 9 – 12: Bayesian approach to hierarchical modeling, model comparison, & hypothesis testing.
• Ch 13: Power & sample size (omit )
• Ch 14: Intro generalized linear model
• Ch 15 – 17: Intro linear regression
• Ch 18 – 19: Oneway & multifactor anova
• Ch 20 – 22: Categorical data analysis, logistic regression, probit regression, poisson regression
Psych 548, Miyamoto, Win '16 18Lee & Wagenmakers, Bayesian Graphical Modeling
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19Psych 548, Miyamoto, Win '16
Bayesian Cognitive Modeling
Lee, M. D., & Wagenmakers, E. J. (2014). Bayesian cognitive modeling: A practical course. Cambridge University Press.
o Michael Lee: http://www.socsci.uci.edu/~mdlee/bgm.html o E. J. Wagenmaker: http://users.fmg.uva.nl/ewagenmakers/BayesCourse/BayesBook.html
o Equivalent Matlab & R code for book are available at the Psych 548 website and at Lee or Wagenmaker's website.
• Emphasis is on Bayesian models of psychological processes rather than on methods of data analysis. Lots of examples.
Chapters in Lee & Wagenmakers
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Table of Contents in Lee & Wagenmakers
20Psych 548:, Miyamoto, Win ‘16 Computer Setup in CSSCR
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21Psych 548, Miyamoto, Win '16
CSSCR Network & Psych 548 Webpage
• Click on /Start /Computer.The path & folder name for your Desktop is:
C:\users\NetID\Desktop (where "NetID" refers to your NetID)
• Double click on MyUW on your Desktop.Find Psych 548 under your courses and double click on the Psych 548 website.
• Download files that are needed for today's class.
Save these files to C:\users\NetID\Desktop o Note that Ctrl-D takes you to your Desktop.
• Run R or RStudio.
Psych 548 Website - END
Is this information obsolete?
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Psych 548 Website
• Point out where to download the material for today’s class
• Point out pdf’s for the textbooks.
Psych 548, Miyamoto, Win '16 22NEXT: Time Permitting ......
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23Psych 548, Miyamoto, Win '16
General Characteristics of Bayesian Inference
• The decision maker (DM) is willing to specify the prior probability of the hypotheses of interest.
• DM can specify the likelihood of the data given each hypothesis.
• Using Bayes Rule, we infer the probability of the hypotheses given the data
Comparison Between Bayesian & Classical Stats - END
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How Does Bayesian Stats Differ from Classical Stats?
Bayesian: Common Aspects• Statistical Models
• Credible Intervals – sets of parameters that have high posterior probability
Bayesian: Divergent Aspects• Given data, compute the full posterior
probability distribution over all parameters
• Generally null hypothesis testing is nonsensical.
• Posterior probabilities are meaningful; p-values are half-assed.
• MCMC approximations to posterior distributions.
Classical: Common Aspects• Statistical Models
• Confidence Intervals – which parameter values are tenable after viewing the data.
Classical: Divergent Aspects• No prior distributions in general, so
this idea is meaningless or self-deluding.
• Null hypothesis te%sting• P-values• MCMC approximations are
sometimes useful but not for computing posterior distributions.
Psych 548, Miyamoto, Win '16 24Sequential Presentation of the Common & Divergent Aspects
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How Does Bayesian Stats Differ from Classical Stats?
Bayesian: Common Aspects• Statistical Models
• Credible Intervals – sets of parameters that have high posterior probability
Bayesian: Divergent Aspects• Given data, compute the full posterior
probability distribution over all parameters
• Generally null hypothesis testing is nonsensical.
• Posterior probabilities are meaningful; p-values are half-assed.
• MCMC approximations to posterior distributions.
Classical: Common Aspects• Statistical Models
• Confidence Intervals – which parameter values are tenable after viewing the data.
Classical: Divergent Aspects• No prior distributions in general, so
this idea is meaningless or self-deluding.
• Null hypothesis te%sting• P-values• MCMC approximations are
sometimes useful but not for computing posterior distributions.
Psych 548, Miyamoto, Win '16 25END
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How Does Bayesian Stats Differ from Classical Stats?
Bayesian: Common Aspects• Statistical Models
• Credible Intervals – sets of parameters that have high posterior probability
Bayesian: Divergent Aspects• Given data, compute the full posterior
probability distribution over all parameters
• Generally null hypothesis testing is nonsensical.
• Posterior probabilities are meaningful; p-values are half-assed.
• MCMC approximations to posterior distributions.
Classical: Common Aspects• Statistical Models
• Confidence Intervals – which parameter values are tenable after viewing the data.
Classical: Divergent Aspects• No prior distributions in general,
so this idea is meaningless or self-deluding.
• Null hypothesis testing• P-values• MCMC approximations are
sometimes useful but not for computing posterior distributions.
Psych 548, Miyamoto, Win '16 26END
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How Does Bayesian Stats Differ from Classical Stats?
Bayesian: Common Aspects• Statistical Models
• Credible Intervals – sets of parameters that have high posterior probability
Bayesian: Divergent Aspects• Given data, compute the full posterior
probability distribution over all parameters
• Generally null hypothesis testing is nonsensical.
• Posterior probabilities are meaningful; p-values are half-assed.
• MCMC approximations to posterior distributions.
Classical: Common Aspects• Statistical Models
• Confidence Intervals – which parameter values are tenable after viewing the data.
Classical: Divergent Aspects• No prior distributions in general, so
this idea is meaningless or self-deluding.
• Null hypothesis testing• P-values• MCMC approximations are
sometimes useful but not for computing posterior distributions.
Psych 548, Miyamoto, Win '16 27END