Acerca de este Curso

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Resultados profesionales del estudiante

29%

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33%

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Nivel intermedio
Aprox. 30 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Habilidades que obtendrás

Gibbs SamplingBayesian StatisticsBayesian InferenceR Programming

Resultados profesionales del estudiante

29%

comenzó una nueva carrera después de completar estos cursos

33%

consiguió un beneficio tangible en su carrera profesional gracias a este curso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 30 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Instructor

ofrecido por

Logotipo de Universidad de California en Santa Cruz

Universidad de California en Santa Cruz

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up93%(1,869 calificaciones)Info
Semana
1

Semana 1

4 horas para completar

Statistical modeling and Monte Carlo estimation

4 horas para completar
11 videos (Total 99 minutos), 4 lecturas, 4 cuestionarios
11 videos
Objectives7m
Modeling process8m
Components of Bayesian models8m
Model specification7m
Posterior derivation9m
Non-conjugate models7m
Monte Carlo integration9m
Monte Carlo error and marginalization6m
Computing examples15m
Computing Monte Carlo error13m
4 lecturas
Module 1 assignments and materials3m
Reference: Common probability distributions
Code for Lesson 3
Markov chains20m
4 ejercicios de práctica
Lesson 120m
Lesson 225m
Lesson 330m
Markov chains20m
Semana
2

Semana 2

5 horas para completar

Markov chain Monte Carlo (MCMC)

5 horas para completar
11 videos (Total 129 minutos), 7 lecturas, 4 cuestionarios
11 videos
Demonstration10m
Random walk example, Part 112m
Random walk example, Part 216m
Download, install, setup3m
Model writing, running, and post-processing12m
Multiple parameter sampling and full conditional distributions8m
Conditionally conjugate prior example with Normal likelihood10m
Computing example with Normal likelihood16m
Trace plots, autocorrelation17m
Multiple chains, burn-in, Gelman-Rubin diagnostic8m
7 lecturas
Module 2 assignments and materials3m
Code for Lesson 4
Alternative MCMC software10m
Code from JAGS introduction
Code for Lesson 510m
Autocorrelation10m
Code for Lesson 6
4 ejercicios de práctica
Lesson 420m
Lesson 530m
Lesson 620m
MCMC45m
Semana
3

Semana 3

6 horas para completar

Common statistical models

6 horas para completar
11 videos (Total 131 minutos), 5 lecturas, 5 cuestionarios
11 videos
Setup in R9m
JAGS model (linear regression)12m
Model checking17m
Alternative models10m
Deviance information criterion (DIC)4m
Introduction to ANOVA10m
One way model using JAGS18m
Introduction to logistic regression6m
JAGS model (logistic regression)18m
Prediction15m
5 lecturas
Module 3 assignments and materials3m
Code for Lesson 7
Code for Lesson 8
Code for Lesson 9
Multiple factor ANOVA20m
5 ejercicios de práctica
Lesson 7 Part A30m
Lesson 7 Part B30m
Lesson 830m
Lesson 945m
Common models and multiple factor ANOVA30m
Semana
4

Semana 4

5 horas para completar

Count data and hierarchical modeling

5 horas para completar
10 videos (Total 106 minutos), 7 lecturas, 4 cuestionarios
10 videos
JAGS model (Poisson regression)17m
Predictive distributions11m
Correlated data8m
Prior predictive simulation10m
JAGS model and model checking (hierarchical modeling)13m
Posterior predictive simulation8m
Linear regression example7m
Linear regression example in JAGS10m
Mixture model in JAGS13m
7 lecturas
Module 4 assignments and materials3m
Prior sensitivity analysis20m
Code for Lesson 10
Normal hierarchical model20m
Applications of hierarchical modeling10m
Code and data for Lesson 11
Mixture model introduction, data, and code20m
4 ejercicios de práctica
Lesson 1040m
Lesson 11 Part A40m
Lesson 11 Part B30m
Predictive distributions and mixture models30m

Revisiones

Principales revisiones sobre BAYESIAN STATISTICS: TECHNIQUES AND MODELS

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