The Program in Measurement, Statistics and Evaluation (EDMS)

is pleased to present its popular three-day short course:

Introduction to Bayesian Statistical Modeling

-- June 7-9, 2017 --

Taught by Roy Levy

 

 

SHORT COURSE DESCRIPTION

This three-day course is intended as both a theoretical and practical introduction to Bayesian statistical modeling. An understanding of Bayesian statistical modeling will be developed by relating it to participants’ existing knowledge of traditional frequentist approaches. It is assumed that participants have expertise with frequentist approaches to statistics (e.g., hypothesis testing, confidence intervals, least-squares and likelihood estimation) in contexts up through multivariate statistical analysis (e.g., multiple regression, MAN(C)OVA, the general linear model for multiple outcomes, logistic or probit modeling for discrete outcomes). Although not required, a participant’s experience in this workshop will be enhanced by additional prior coursework or experience with advanced modeling techniques such as factor analysis, item response theory, structural equation modeling, and multilevel modeling, and/or by familiarity with the basics of probability theory (e.g., joint, marginal, and conditional distributions, independence). No prior experience with Bayesian statistical modeling is required. The philosophical underpinnings and departures from conventional frequentist interpretations of probability will be explained. This in turn will motivate the development of Bayesian statistical modeling.

To introduce Bayesian principles in familiar contexts we will begin with simple binomial and univariate normal models, then move to regression, and then give examples of more complex models including factor analysis, item response models, structural equation models, and Bayesian networks. Along the way, we will cover aspects of modeling including model construction, graphical representations of models, Markov chain Monte Carlo (MCMC) estimation, evaluating hypotheses and data-model fit, model comparisons, and modeling in the presence of missing data. Although Bayesian statistical modeling has proven advantageous in many disciplines, the examples used in presentations draw primarily from social science research, including the fields of education and assessment. Examples will be accompanied by input and output from BUGS, JAGS, R, and Netica software packages. Throughout the course participants will be able to practice exercises using these software packages; participants are strongly encouraged to bring their own laptop PCs to perform these exercises. (Participants will be instructed on how to download free versions of the software prior to the course.)

WHAT IS BAYESIAN STATISTICAL MODELING?

Bayesian approaches to statistical modeling and inference are characterized by treating all entities (observed variables, model parameters, missing data, etc.) as random variables characterized by distributions. In a Bayesian analysis, all unknown entities are assigned prior distributions that represent our thinking prior to observing the data. Once values for the data are observed, Bayes’ theorem is employed to update the prior distributions, yielding posterior distributions. Posterior distributions constitute the solution to the analysis and can be summarized using point or interval summaries and interpreted via probability-based reasoning. This approach to modeling departs, both practically and philosophically, from traditional frequentist methods that constitute the majority of statistical training. Importantly, adopting a Bayesian approach allows an analyst to accomplish statistical and inferential goals that cannot be attained by, or pose considerable challenges to, conventional frequentist approaches, and recent computational advances now allow researchers access to this wider class of models. Examples of such modeling complexities include dynamically updating beliefs based on the arrival of data over time (e.g., revising a medical diagnosis based on test results), incorporating prior beliefs about the modeled phenomena, modeling highly dependent multivariate systems with multiple sources of uncertainty (e.g., weather forecasting), estimating scores on latent variables (e.g., factor scores, latent class membership), dealing with nonlinearities in parameters (e.g., mediation effects in longitudinal data, interactions of latent variables), and analyzing datasets with small sample sizes. Indeed, a number of sophisticated analyses in frequentist approaches are, in fact, explicitly Bayesian or approximations to Bayesian analyses (e.g., multiple imputation for missing data, multilevel modeling of clustered observations). Even in simple modeling situations, a Bayesian approach allows for probability-based reasoning that is more natural and extensible than the inferential machinery of frequentist approaches. Bayesian statistical modeling and inference is an attractive alternative to frequentist approaches in that a Bayesian perspective offers a coherent approach to statistical modeling, including building and fitting models with complex features, interpreting results, making inferences, and representing uncertainty.

TARGET AUDIENCE

Research professionals, faculty, and graduate students

REQUISITE KNOWLEDGE

It is assumed that participants have expertise with frequentist approaches to statistics (e.g., hypothesis testing, confidence intervals, least-squares and likelihood estimation) in contexts up through multivariate statistical analysis (e.g., multiple regression, MAN(C)OVA, the general linear model for multiple outcomes, logistic or probit modeling for discrete outcomes). Although not required, a participant’s experience in this workshop will be enhanced by additional prior coursework or experience with advanced modeling techniques such as factor analysis, item response theory, structural equation modeling, and multilevel modeling, and/or by familiarity with the basics of probability theory (e.g., joint, marginal, and conditional distributions, independence). No prior experience with Bayesian statistical modeling is required.

DATES AND TIMES

June 7-9, 2017 (Wednesday – Friday), Check in at 8:30AM

Continental Breakfast*:  8:30 AM – 9:00 AM
Morning Session:           9:00 AM – 12:30 PM
Lunch (on your own):    12:30 PM – 1:30 PM
Afternoon Session:        1:30 PM – 5:00 PM

* Participants who have special dietary needs or preferences are welcome to bring their own food as well.

LOCATION

Benjamin Banneker Room
Adele H. Stamp Student Union
University of Maryland
College Park, MD  20742

Link to campus maps

COURSE FEES
(seats limited)

$595 for all three days
$395 for full-time students
(free for pre-registered HDQM Department faculty and students, although you must register; admission will depend on if space is available)

ON-LINE OPTION! $195 for all three days. See below for details.

ON-LINE OPTION

Format:  On-line participants will receive a personalized login code to use their own computer, browser, and reliable Wi-Fi connection to access a live-stream of the workshop, showing the instructor as well as the handouts displayed on screen to on-site participants.

Materials:   On-line participants will receive electronic copies of the workshop materials that on-site participants receive, as well as any other relevant materials or information.

Timing/access: On-line participants may choose to watch the stream synchronously, or may elect to watch a recording of the workshop asynchronously, or both.  With DVR-like capabilities, and access to the recordings for up to one-week after the start of the workshop, this format allows on-line participants to choose when they engage.  This is especially useful for on-line participants in different time zones, or anywhere in the world, who may choose to watch at some later time than the actual workshop time. (Asynchronous participation does not include real-time chat with other on-line participants, although a visual record of prior chats will be viewable).

Technical support: Participants are assumed to be able to secure a reliable computer, internet browser, and Wi-Fi connection.  Challenges at the user end must be resolved by the user.  Fortunately, because the workshop is recorded, users experiencing technical challenges can still “catch up” by watching the recordings to which they have access.

Content support: Content support for on-line participants is limited to chat with the on-line participant community and any quantitative methodology doctoral students who might also be participating.  There is no mechanism for submitting individual questions to the instructor.
Hands-on activities: On-line participants may choose to try any hands-on activities being done by on-site participants; however, support for such activities is limited to the on-line participant community.

Cost:  $195 for three days of streaming content, access to the workshop recordings for one week total, and electronic copies of all materials.

HOW TO REGISTER

Please register using the preferred on-line registration form:
On-line short-course registration form
(Note: For those students who are not in the HDQM department please fill out the paper registration form and upload it when prompted within the on-line registration system).

For those who prefer not to use the on-line registration system, please complete and submit the following paper registration form:
Paper short-course registration form

Note that it may take up to 7-10 business days for your registration to be processed.

QUESTIONS?

Contact Ms. Monica Morell -- Bayesian17.workshop@gmail.com

NEARBY AIRPORTS

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DIRECTIONS: DRIVING

From Baltimore and Points North

  • Take I-95 South to Washington, D.C.'s Capital Beltway (I-495).
  • Take Exit 27 and then follow signs to Exit 25 (U.S. 1 South toward College Park).
  • Proceed approximately two miles south on U.S. Route 1.
  • Turn right into Campus Drive entrance (main gate).
  • Proceed up Campus Dr to the "M" circle, go halfway around the circle and continue on Campus Dr (second right after entering the circle).
  • Proceed up Campus Dr to the "M" circle, go halfway around the circle and continue on Campus Dr (second right after entering the circle) until you see Stamp Student Union on your right side.

From Virginia and Points South

  • Take I-95 North to Washington, D.C.'s Capital Beltway (I-495).
  • Continue North on I-95/I-495 toward Baltimore.
  • Take Exit 25 (U.S. 1 South toward College Park).
  • Proceed approximately two miles south on U.S. Route 1.
  • Turn right into Campus Drive entrance (main gate).
  • Proceed up Campus Dr to the "M" circle, go halfway around the circle and continue on Campus Dr (second right after entering the circle) until you see Stamp Student Union on your right side.

From Virginia and Points West

  • Take I-66 East or I-270 South to Washington, D.C.'s Capital Beltway (I-495).
  • Go East on I-495 toward Baltimore/Silver Spring.
  • Take Exit 25 (U.S. 1 South toward College Park).
  • Proceed approximately two miles south on U.S. Route 1.
  • Turn right into Campus Drive entrance (main gate).
  • Proceed up Campus Dr to the "M" circle, go halfway around the circle and continue on Campus Dr (second right after entering the circle) until you see Stamp Student Union on your right side.

From Annapolis and Points East

  • Take U.S. 50 to Washington, D.C.'s Capital Beltway (I-495).
  • Go North on I-95/I-495 toward Baltimore.
  • Take Exit 25 (U.S. 1 South toward College Park).
  • Proceed approximately two miles south on U.S. Route 1.
  • Turn right into Campus Drive entrance (main gate).
  • Proceed up Campus Dr to the "M" circle, go halfway around the circle and continue on Campus Dr (second right after entering the circle) until you see Stamp Student Union on your right side.

From Washington, D.C. (Northwest/Southwest)

  • Take 16th St. North which becomes Georgia Ave. North at Maryland/D.C. line.
  • Go East on I-495 toward Baltimore.
  • Take Exit 25 (U.S. 1 South toward College Park).
  • Proceed approximately two miles south on U.S. Route 1.
  • Turn right into Campus Drive entrance (main gate).
  • Proceed up Campus Dr to the "M" circle, go halfway around the circle and continue on Campus Dr (second right after entering the circle) until you see Stamp Student Union on your right side.

From Washington, D.C. (Northeast/Southeast)

  • Take Rhode Island Ave. (U.S. 1 North) which becomes Baltimore Ave. North at Maryland/D.C. line.
  • Proceed through the city of College Park.
  • Turn left at Campus Drive entrance (main gate).
  • Proceed up Campus Dr to the "M" circle, go halfway around the circle and continue on Campus Dr (second right after entering the circle) until you see Stamp Student Union on your right side.

VISITOR PARKING

Participants may park at the Union Lane Garage (located between the Adele H. Stamp Student Union and Cole Field House) for a daily fee. There are numerous metered spaces on campus but the University police are diligent about ticketing cars at expired meters as well as cars without appropriate stickers in reserved parking lots.

More information about parking on University of Maryland Campus can be found at the web site: http://www.cvs.umd.edu/visitors/parking.html

METRO (SUBWAY)

The Campus is conveniently located approximately 1 mile from the College Park-University of Maryland Metro Station. The stop is on the green line of the D.C. Metro System. The University of Maryland Shuttle Bus runs from the College Park Metro stop on a twenty-minute schedule through the Campus. Or, a brisk twenty minute walk up a moderate hill through the Campus will bring you to all locations.

D.C. Metro Map: https://www.wmata.com/schedules/maps/

ACCOMMODATIONS

Participants are responsible for arranging their own accommodations. For out-of-town guests, there are several sources of accommodations in the immediate area. Information about hotel pricing and reservations can be found at the web site: http://www.cvs.umd.edu/visitors/offcampus.html. Note that participants will need to make their own arrangements for transportation to and from campus.

[Note that there is a hotel located on the edge of the University of Maryland campus: The Marriott Inn & Conference Center, University of Maryland University College. For more information about this hotel, visit: http://www.marriott.com/hotels/travel/wasum-college-park-marriott-hotel-and-conference-center/

THE INSTRUCTOR

Dr. Roy Levy is an Associate Professor of Measurement and Statistical Analysis in the T. Denny Sanford School of Social and Family Dynamics at Arizona State University, where he teaches coursework in Bayesian statistical modeling. He is the co-author of the book, Bayesian Psychometric Modeling, and his research has appeared in such journals as Structural Equation Modeling: A Multidisciplinary Journal, British Journal of Mathematical and Statistical Psychology, Psychological Methods, Multivariate Behavioral Research, Applied Psychological Measurement, Journal of Educational and Behavioral Statistics, Sociological Methods and Research, Educational and Psychological Measurement, and Journal of Probability and Statistics. He is a past chair of the structural equation modeling special interest group of the American Educational Research Association, has served on the editorial boards of several journals, and was a 2010 recipient of the Presidential Early Career Award for Scientists and Engineers by the President of the United States. Dr. Levy holds a B.A. in Philosophy, an M.A. in Measurement, Statistics and Evaluation, and a Ph.D. in Measurement, Statistics and Evaluation from the University of Maryland.