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.
Ce cours fait partie de la Spécialisation Statistiques bayésiennes
Offert par
Spécialisation Statistiques bayésiennesUniversité de Californie à Santa CruzÀ propos de ce cours
Compétences que vous acquerrez
- Gibbs Sampling
- Bayesian Statistics
- Bayesian Inference
- R Programming
Offert par
Université de Californie à Santa Cruz
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.
Programme de cours : ce que vous apprendrez dans ce cours
Statistical modeling and Monte Carlo estimation
Statistical modeling, Bayesian modeling, Monte Carlo estimation
Markov chain Monte Carlo (MCMC)
Metropolis-Hastings, Gibbs sampling, assessing convergence
Common statistical models
Linear regression, ANOVA, logistic regression, multiple factor ANOVA
Count data and hierarchical modeling
Poisson regression, hierarchical modeling
Avis
- 5 stars83,10 %
- 4 stars12,83 %
- 3 stars2,25 %
- 2 stars0,90 %
- 1 star0,90 %
Meilleurs avis pour BAYESIAN STATISTICS: TECHNIQUES AND MODELS
Great course. The instructor provided detailed code examples and clear explanations for model intuitions. The final capstone project is a plus.
Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course
The course was really interesting and the codes were easy to follow. Although I did take the previous course for this series, I still found it hard to grasp the concepts immediately.
Very interesting.
I would like to have a follow on since the possible applications of the topics explained in the course.
À propos du Spécialisation Statistiques bayésiennes
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.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Une aide financière est-elle possible ?
D'autres questions ? Visitez le Centre d'Aide pour les Étudiants.
