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Bayesian Computational Analysis with R

By: Geoffrey Hubona

  • 11:38:09
  • 90
  • 20
  • Language: English

Course Summary

Bayesian Computational Analyses with R is an introductory course on the use and implementation of Bayesian modeling using R software. The Bayesian approach is an alternative to the "frequentist" approach where one simply takes a sample of data and makes inferences

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Target Audience

  • The course is ideal for anyone interested in learning both the conceptual and practical side of using Bayes' Rule to model likely event outcomes.
  • The course is best suited for both students and professionals who currently make use of quantitative or probabilistic modeling.
  • It is useful to have a working knowledge of either basic inferential statistics or probability theory.
  • It is NOT necessary to have prior experience using R software to successfully complete and to benefit from this course.

Pre-Requisites

  • Students will need to install R and RStudio software, but ample instruction for doing so is provided in the course materials.

Curriculum

  • Exercise Files
  • Introduction to Bayesian Computational Analyses with R
    02:00
  • Introduction to Course Materials
    02:19
  • Introduction to R Software (slides, part 1)
    09:54
  • Introduction to R Software (slides, part 2)
    08:43
  • Introduction to R Software (slides, part 3)
    12:16
  • Introduction to R Software with Scripts (part 1)
    07:25
  • Introduction to R Software with Scripts (part 2)
    09:52
  • Introduction to R Software with Scripts (part 3)
    11:50
  • Introduction to R Software with Scripts (part 4)
    08:38
  • Introduction to R Software with Scripts (part 5)
    07:40
  • Programming a Monte Carlo Simulation
    10:13
  • Section 1 R Scripting Exercises
    00:22
  • Exercise Files
  • More on the Course and Materials
    02:19
  • Session 1 R Scripting Exercise Solutions
    12:32
  • Background on Probability Density Functions (PDFs)
    08:45
  • Normal dnorm() Functions (part 1)
    04:15
  • Normal dnorm() Functions (part 2)
    05:08
  • Normal pnorm() Function
    10:18
  • Bayes' Rule and More
    11:37
  • Likelihood Function
    06:58
  • Using Discrete Priors (part 1)
    08:32
  • Using Discrete Priors (part 2)
    06:34
  • Using a Beta Prior (part 1)
    05:12
  • Using a Beta Prior (part 2)
    07:10
  • Using a Beta Prior (part 3)
    05:05
  • Simulating Beta Posteriors
    04:38
  • Brute Force Posterior Simulation using Histogram Prior
    08:43
  • Predictive Priors (slides)
    06:24
  • Predictive Priors (scripts, part 1)
    07:23
  • Predictive Priors (scripts, part 2)
    07:19
  • Section 2 Exercises
    02:20
  • Section 2 Exercise Solution
    10:59
  • Exercise Files
  • Prelude to Single Parameter Models
    11:17
  • Single Parameter Models
    10:59
  • Heart Transplant Mortality Rate (part 1)
    12:26
  • Heart Transplant Mortality Rate (part 2)
    10:40
  • Test of Bayesian Robustness (part 1)
    10:23
  • Test of Bayesian Robustness (part 2)
    11:02
  • Exercise: How Many Taxis?
    03:37
  • Exercise Solution: How Many Taxis?
    06:15
  • Exercise Files
  • Conjugate Mixtures (part 1)
    10:37
  • Conjugate Mixtures (part 2)
    09:19
  • A Bayesian Test of the Fairness of a Coin (part 1)
    07:42
  • A Bayesian Test of the Fairness of a Coin (part 2)
    10:06
  • More on the Fairness of a Coin (part 3)
    11:30
  • Introduction to Probability Density Functions (part 1)
    13:26
  • Intro to PDFs (part 2)
    06:55
  • Intro to PDFs (part 3)
    07:14
  • Exercise Files
  • Mortality Rate Exercise Solution (part 1)
    08:07
  • Mortality Rate Exercise Solution (part 2)
    07:53
  • Normal Multiparameter Models (part 1)
    11:03
  • Normal Multiparameter Models (part 2)
    08:21
  • Normal Multiparameter Models (part 3)
    07:59
  • Multinomial Multiparameter Models (part 1)
    10:23
  • Multinomial Multiparameter Models (part 2)
    11:34
  • Bioassay Experiment (part 1)
    10:32
  • Bioassay Experiment (part 2)
    10:47
  • Exercise: Comparing Two Proportions
    03:11
  • Exercise Solution: Comparing Two Proportions (part 1)
    05:13
  • Exercise Solution: Comparing Two Proportions (part 2)
    11:23
  • Exercise Files
  • Introduction to Bayesian Computation Section
    06:08
  • Computing Integrals to Estimate a Probability (part 1)
    11:21
  • Computing Integrals to Estimate a Probability (part 2)
    10:20
  • A Beta-Binomial Model of Overdispersion (part 1)
    10:57
  • A Beta-Binomial Model of Overdispersion (part 2)
    10:50
  • Exercise: Inference About a Normal Population
    02:55
  • Exercise Solution: Inference about a Normal Population
    09:40
  • Exercise Files
  • Rejection Sampling (part 1)
    10:07
  • Rejection Sampling (part 2)
    10:02
  • Rejection Sampling (part 3)
    06:42
  • Rejection Sampling (part 4)
    06:54
  • Rejection Sampling (part 5)
    10:40
  • Rejection Sampling (part 6)
    09:46
  • Importance Sampling
    08:26
  • Exercise Files
  • One-Sided Test of a Normal Mean (part 1)
    09:51
  • One-Sided Test of a Normal Mean (part 2)
    09:41
  • One-Sided Test of a Normal Mean (part 3)
    07:44
  • Two-Sided Test of a Normal Mean
    11:33
  • Streaky Behavior (part 1)
    12:34
  • Streaky Behavior (part 2)
    10:26
  • Streaky Behavior (part 3)
    09:32
  • Streaky Behavior (part 4)
    08:44

About the Author

Geoffrey Hubona, Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

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Bayesian Computational Analysis with R

  • 11:38:09
  • 90
  • 20
  • Language: English
  • 15 days Money back Gurantee
  • Unlimited Access
  • Android, iPhone and iPad Access
  • Certificate of Completion

Course Summary

Bayesian Computational Analyses with R is an introductory course on the use and implementation of Bayesian modeling using R software. The Bayesian approach is an alternative to the "frequentist" approach where one simply takes a sample of data and makes inferences

Read More

Target Audience

  • The course is ideal for anyone interested in learning both the conceptual and practical side of using Bayes' Rule to model likely event outcomes.
  • The course is best suited for both students and professionals who currently make use of quantitative or probabilistic modeling.
  • It is useful to have a working knowledge of either basic inferential statistics or probability theory.
  • It is NOT necessary to have prior experience using R software to successfully complete and to benefit from this course.

Pre-Requisites

  • The course is ideal for anyone interested in learning both the conceptual and practical side of using Bayes' Rule to model likely event outcomes.
  • The course is best suited for both students and professionals who currently make use of quantitative or probabilistic modeling.
  • It is useful to have a working knowledge of either basic inferential statistics or probability theory.
  • It is NOT necessary to have prior experience using R software to successfully complete and to benefit from this course.

About the Author

Geoffrey Hubona, Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

More From Author