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Bayesian Inference: Methods and Applications

Current Offerings

Bayesian Inference: Methods and Applications (646): The course aims to develop a thorough understanding of Bayesian inference, with a special focus on empirical applications in marketing. The course will start with a brief theoretical foundation to Bayesian inference and will subsequently focus on empirical methods. Initial topics would include Bayesian linear regression, multivariate regression, importance sampling and its applications. Subsequently, the course will focus on Markov Chain Monte Carlo (MCMC) methods including the Gibbs Sampler and the Metropolis-Hastings algorithm and their applications. The overall focus of the course will be on applying these methods for empirical research using a programming language such as R.

Sections

Bayesian Inference: Methods and Applications | MKTG 646 Section 01 Class #28111

  • Sridhar Narayanan
  • 3 Units

Notes: MBA students need instructor permission. P/F Not Allowed. Open to GSB PhD students. No Exam. 6 Individual Projects/Papers. Participation 20% Projects/Papers 80%. Seminar. Mandatory attendance. Absences impact grade. Non-GSB students: See gsb.stanford.edu/NonGSBReg.

Past Offerings

Bayesian Inference: Methods and Applications (646): The course aims to develop a thorough understanding of Bayesian inference, with a special focus on empirical applications in marketing. The course will start with a brief theoretical foundation to Bayesian inference and will subsequently focus on empirical methods. Initial topics would include Bayesian linear regression, multivariate regression, importance sampling and its applications. Subsequently, the course will focus on Markov Chain Monte Carlo (MCMC) methods including the Gibbs Sampler and the Metropolis-Hastings algorithm and their applications. The overall focus of the course will be on applying these methods for empirical research using a programming language such as R.

Sections

Bayesian Inference: Methods and Applications | MKTG 646 Section 01 Class #28025

  • Sridhar Narayanan
  • 3 Units
  • 2025-2026 Autumn
    Schedule No Longer Available

Notes: Non-GSB Students: See gsb.stanford.edu/NonGSBReg. No Exam. Open to GSB PhD students. P/F Not Allowed. MBA2 students need instructor permission. 6 Individual Projects/Papers. Mandatory attendance. Absences impact grade. Non-GSB students: See gsb.stanford.edu/NonGSBReg. Seminar. Participation 20% Projects/Papers 80%.

  • 2025-2026 Autumn
    Schedule No Longer Available

Bayesian Inference: Methods and Applications (646): The course aims to develop a thorough understanding of Bayesian inference, with a special focus on empirical applications in marketing. The course will start with a brief theoretical foundation to Bayesian inference and will subsequently focus on empirical methods. Initial topics would include Bayesian linear regression, multivariate regression, importance sampling and its applications. Subsequently, the course will focus on Markov Chain Monte Carlo (MCMC) methods including the Gibbs Sampler and the Metropolis-Hastings algorithm and their applications. The overall focus of the course will be on applying these methods for empirical research using a programming language such as R.

Sections

Bayesian Inference: Methods and Applications | MKTG 646 Section 01 Class #29377

  • Sridhar Narayanan
  • 3 Units
  • 2024-2025 Autumn
    Schedule No Longer Available

Notes: All Non GSB students should be graduate students. No Exam. 5 Individual Projects/Papers. Open to GSB PhD students. P/F Not Allowed. Participation 20% Projects/Papers 80%. MBA students need instructor permission. Mandatory attendance. Absences impact grade. Non-GSB students: See gsb.stanford.edu/NonGSBReg. Seminar.

  • 2024-2025 Autumn
    Schedule No Longer Available

Every effort is made to ensure that the degree requirement and course information, applicable policies, and other materials contained in the SLS Approved Non-Law Courses are accurate and current. The University reserves the right to make changes at any time without prior notice.

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