İlgili konular
Bu kursta neler var
- 17 sa 49 dk video
- 29 ders
- Seviye: Orta
- Dil: English
- Nerede kaldığını hatırlıyoruz
- Tamamen ücretsiz, kartsız
Dersler
3 bölüm · 29 ders · 17 sa 49 dk toplam süre
1–10. dersler10 ders · 6 sa 29 dk
- BDA course 1.1 Introduction to uncertainty and modelling18 dk
- BDA course 1.2 Introduction to the course contents9 dk
- BDA 2019 Lecture 2.1 Bayesian inference, observation model, likelihood, posterior, and binomial37 dk
- BDA 2019 Lecture 2.2 priors and prior information, and one parameter normal model44 dk
- BDA course 2 extra: likelihood, normalization, density, model M14 dk
- BDA 2019 Lecture 3 on multiparameter models. joint, marginal and conditional distribution, normal1 sa 26 dk
- BDA 2019 Lecture 4.1 numerical issues, Monte Carlo, how many simulation draws are needed, ...50 dk
- BDA 2019 Lecture 4.2 direct simulation, curse of dimensionality, rejection and importance sampling43 dk
- BDA 2019 Lecture 5.1 Markov chain Monte Carlo, Gibbs sampling, and Metropolis algorithm51 dk
- BDA 2019 Lecture 5.2 warm up, convergence diagnostics, R-hat, and effective sample size38 dk
11–20. dersler10 ders · 6 sa 36 dk
- BDA 2019 Lecture 6.1 HMC, NUTS, dynamic HMC, and HMC specific convergence diagnostics50 dk
- BDA 2019 Lecture 6.2 probabilistic programming and Stan46 dk
- Stan and probabilistic programming introduction39 dk
- BDA 2019 Lecture 7.1 hierarchical models44 dk
- BDA 2019 Lecture 7.2 exchangeability16 dk
- BDA 2019 Lecture 8.1 model checking47 dk
- BDA 2019 Lecture 8.2 cross-validation part 146 dk
- BDA 2019 Lecture 9.1 PSIS-LOO and K-fold cross-validation51 dk
- BDA 2019 Lecture 9.2 model comparison and selection30 dk
- BDA 2019 Lecture 9.3 extra lecture on projection predictive variable selection26 dk
21–29. dersler9 ders · 4 sa 45 dk
- BDA 2019 Lecture 10.1 Decision analysis51 dk
- BDA 2019 Lecture 11.1 Normal approximation, Laplace approximation.44 dk
- BDA 2019 Lecture 11.2 Large sample theory and counter examples43 dk
- BDA 2019 Lecture 12.1 Frequency evaluation, hypothesis testing, and variable selection55 dk
- BDA 2019 Lecture 12.2 Summary of modeling data collection, linear models, Lasso, and Gaussian proces48 dk
- BDA course 2.1 Observation model, likelihood, posterior, and binomial model15 dk
- BDA course 2.2 Predictive distribution and benefit of integration8 dk
- BDA course 2.3 Priors and prior information20 dk
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Ne gerekiyor
- Temel bilgi işe yarar: kurs temellerin ötesinden devam ediyor.
Açıklama
Bayesian Data Analysis course videos https://avehtari.github.io/BDA_course_Aalto/
Bu kurs kimin için
- Veri ve AI temellerini bilenler.
Bu iki madde kursun seviyesinden çıkardığımız sonuç. Açıklama yazarın kendisine ait.