All Questions
5
questions
2
votes
0
answers
73
views
MCMC fitting of Dirichlet Process or Polya Tree prior to residuals in (simple linear regression)/(2-independent-samples) problem
Consider a simple location-shift semi-parametric model with two mutually-independent samples (in what follows, $F$ is a cumulative distribution function (CDF) on $\mathbb{ R }$, the $C_i$ and $T_j$ ...
- 69
2
votes
1
answer
854
views
KNN as a crude prototype of Gaussian Process Regression?
I've heard it said before that K-Means-Clustering is a prototypical method for Expectation-Maximization algorithm. Where KM Clustering returns a hard cluster assignment, EM returns soft assignments, ...
- 3,382
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
0
votes
1
answer
267
views
Understanding Gaussian Process and their Priors
I am very interested to understand the motivation behind why are we using these priors let's say in the context of regression. I know that the kernel depicts the distance between the points or let's ...
- 3
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