All Questions
6
questions
4
votes
1
answer
52
views
In what ways is Gaussian Process Regression both parametric and non-parametric?
Gaussian Process Regression is considered a "non-parametric" model. However, the term "non-parametric" is often used imprecisely to mean different things, leading to questions ...
- 847
11
votes
1
answer
514
views
Do Stochastic Processes such as the Gaussian Process/Dirichlet Process have densities? If not, how can Bayes rule be applied to them?
The Dirichlet Pocess and Gaussian Process are often referred to as "distributions over functions" or "distributions over distributions". In that case, can I meaningfully talk about the density of a ...
2
votes
0
answers
133
views
Smooth regression algorithms that produce zero training error
I am looking to fit three regression functions $f_1, f_2, f_3:\mathbb{R}^2 \to \mathbb{R}$. For example, let's say $X_1$ is time, $X_2$ is geographic latitude, $f_1$ is the temperature, $f_2$ is the ...
- 201
8
votes
2
answers
2k
views
Bayesian nonparametric answer to deep learning?
As I understand it, deep neural networks are performing "representation learning" by layering features together. This allows learning very high dimensional structures in the features. Of course, it's ...
- 1,002
8
votes
1
answer
1k
views
Nonparametric nonlinear regression with prediction uncertainty (besides Gaussian Processes)
What are state-of-the-art alternatives to Gaussian Processes (GP) for nonparametric nonlinear regression with prediction uncertainty, when the size of the training set starts becoming prohibitive for ...
- 5,226
4
votes
1
answer
133
views
Learning parameters of non-parametric Bayesian models
I have a sample of Chinese restaurant process which I want to model as Pitman–Yor process. How do I determine parameters of Pitman-Yor model from given sample?
For Dirichlet process I would just use ...
- 389