This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data.
이 강좌에 대하여
귀하가 습득할 기술
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.
- 5 stars83.29%
- 4 stars12.64%
- 3 stars2.25%
- 2 stars0.90%
- 1 star0.90%
BAYESIAN STATISTICS: TECHNIQUES AND MODELS의 최상위 리뷰
Excellent course for introducing yourself to Monte Carlo Methods applied to Bayesian statistics. Highly recommended!
I really liked the course. It was well organized. The fact that the theory was accompanied by hands-on exercises in R truly reinforced the concept. Well-done!
Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course
terrific, so I've learn quite a lot basic knowledge about MCMC. So I can build kinds of models with better understanding.
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.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.