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: Raquel Prado
4,909 already enrolled
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(16 reviews)
Recommended experience
Intermediate level
Familiarity with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
(16 reviews)
Recommended experience
Intermediate level
Familiarity with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
Build models that describe temporal dependencies.
Use R for analysis and forecasting of times series.
Explain stationary time series processes.
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This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models.
Time series analysis is concerned with modeling the dependency among elements of a sequence of temporally related variables. To succeed in this course, you should be familiar with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference. You will learn how to build models that can describe temporal dependencies and how to perform Bayesian inference and forecasting for the models. You will apply what you've learned with the open-source, freely available software R with sample databases. Your instructor Raquel Prado will take you from basic concepts for modeling temporally dependent data to implementation of specific classes of models
This module defines stationary time series processes, the autocorrelation function and the autoregressive process of order one or AR(1). Parameter estimation via maximum likelihood and Bayesian inference in the AR(1) are also discussed.
9 videos12 readings4 assignments1 peer review
This module extends the concepts learned in Week 1 about the AR(1) process to the general case of the AR(p). Maximum likelihood estimation and Bayesian posterior inference in the AR(p) are discussed.
9 videos8 readings2 assignments1 peer review
Normal Dynamic Linear Models (NDLMs) are defined and illustrated in this module using several examples. Model building based on the forecast function via the superposition principle is explained. Methods for Bayesian filtering, smoothing and forecasting for NDLMs in the case of known observational variances and known system covariance matrices are discussed and illustrated.
10 videos7 readings2 assignments1 peer review
7 videos4 readings2 assignments1 peer review
In this final project you will use normal dynamic linear models to analyze a time series dataset downloaded from Google trend.
1 peer review
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Reviewed on Feb 5, 2024
It was a nice course, but it would be better if there were more supplementary materials for the proof and theoretical discussion.
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