What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of Bayesian Statistics Specialization
Instructor: Jizhou Kang
Included with
Recommended experience
Advanced level
Familiarity with calculus-based probability, maximum-likelihood estimation, Bayesian inference, mixture models, and time-series analysis.
Recommended experience
Advanced level
Familiarity with calculus-based probability, maximum-likelihood estimation, Bayesian inference, mixture models, and time-series analysis.
Demonstrate a wide range of skills and knowledge in Bayesian statistics.
Explain essential concepts in Bayesian statistics.
Apply what you know to real-world data.
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6 assignments
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This is the capstone project for UC Santa Cruz's Bayesian Statistics Specialization. It is an opportunity for you to demonstrate a wide range of skills and knowledge in Bayesian statistics and to apply what you know to real-world data. You will review essential concepts in Bayesian statistics with lecture videos and quizzes, and you will perform a complex data analysis and compose a report on your methods and results.
In this module, we will introduce conjugate Bayesian analysis for the autoregressive (AR) models.
3 videos7 readings2 assignments
In this module, we will introduce some criteria that can be used in selecting the order of AR processes and the number of mixing components, which will be used later when we introduce mixture of AR models.
2 videos2 readings2 assignments
In this module, we will perform Bayesian analysis for location mixture of AR(p) models.
4 videos3 readings2 assignments
In this module, we will use everything we have learned up until now to perform a mixture model on time series data.
1 reading1 peer review
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Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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