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.
提供方
贝叶斯统计 專項課程加州大学圣克鲁兹分校課程信息
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可分享的證書
完成後獲得證書
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第 4 門課程（共 5 門）
中級
Familiarity with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
完成時間大約為22 小時
英語（English）
字幕：英語（English）
您將獲得的技能
- Bayesian Statistics
- Forecasting
- Dynamic Linear Modeling
- Time Series
- R Programming
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 4 門課程（共 5 門）
中級
Familiarity with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
完成時間大約為22 小時
英語（English）
字幕：英語（English）
提供方
加州大学圣克鲁兹分校
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.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 6 小時
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.
完成時間為 6 小時
9 個視頻 （總計 94 分鐘）, 12 個閱讀材料, 5 個測驗
完成時間為 5 小時
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.
完成時間為 5 小時
9 個視頻 （總計 96 分鐘）, 8 個閱讀材料, 3 個測驗
完成時間為 5 小時
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.
完成時間為 5 小時
10 個視頻 （總計 114 分鐘）, 7 個閱讀材料, 3 個測驗
完成時間為 4 小時
Week 4: Normal dynamic linear models, Part II
完成時間為 4 小時
7 個視頻 （總計 103 分鐘）, 4 個閱讀材料, 3 個測驗
關於 贝叶斯统计 專項課程
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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