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.
제공자:
이 강좌에 대하여
Familiarity with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
귀하가 습득할 기술
- Bayesian Statistics
- Forecasting
- Dynamic Linear Modeling
- Time Series
- R Programming
Familiarity with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
제공자:

캘리포니아대학교 산타크루스캠퍼스
UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience.
강의 계획표 - 이 강좌에서 배울 내용
Week 1: Introduction to time series and the AR(1) process
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.
Week 2: The AR(p) process
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.
Week 3: Normal dynamic linear models, Part I
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.
Week 4: Normal dynamic linear models, Part II
베이지안 통계 특화 과정 정보
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.

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