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Introduction
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Classroom Contents
Learning What We Know and Knowing What We Learn - Gaussian Process Priors for Neural Data Analysis
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- 1 Introduction
- 2 Why should we use latent variable models
- 3 Fitting variable models
- 4 Data hungry
- 5 Simple regression
- 6 Bayesian inference
- 7 Covariance
- 8 Covariance kernels
- 9 Spectral mixture kernels
- 10 Margin likelihood
- 11 Correlation kernel
- 12 Factor analysis
- 13 Collab notebook
- 14 Challenges
- 15 Bayesian GPFA
- 16 Data limitations
- 17 Results
