All Questions
Tagged with nonparametric machine-learning
54
questions
53
votes
9
answers
3k
views
Are all models useless? Is any exact model possible -- or useful?
This question has been festering in my mind for over a month. The February 2015 issue of Amstat News contains an article by Berkeley Professor Mark van der Laan that scolds people for using inexact ...
- 20.8k
44
votes
4
answers
69k
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What exactly is the difference between a parametric and non-parametric model?
I am confused with the definition of non-parametric model after reading this link Parametric vs Nonparametric Models and Answer comments of my another question.
Originally I thought "parametric vs ...
- 37.2k
37
votes
4
answers
49k
views
What is the weak side of decision trees?
Decision trees seems to be a very understandable machine learning method.
Once created it can be easily inspected by a human which is a great advantage in some applications.
What are the practical ...
- 1,412
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 ...
10
votes
1
answer
9k
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Why KNN and SVM with a gaussian are non-parametric models?
I was told that these two are non-parametric models. But I can't figure out why, especially for KNN.
Could anyone answer my questions?
- 653
10
votes
2
answers
1k
views
Best methods of feature selection for nonparametric regression
A newbie question here. I am currently performing a nonparametric regression using the np package in R. I have 7 features and using a brute force approach I identified the best 3. But, soon I will ...
- 101
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
7
votes
1
answer
309
views
Kendall-tau and RKHS spaces
Given two random variables $X_1$ and $X_2$, the Kendall-tau correlation coefficient could be defined as $$ \tau(X_{1},X_{2})=\mathbb{P}\Big((X_{1}-\tilde{X}_{1})(X_{2}-\tilde{X}_{2})>0\Big)-\mathbb{...
- 171
5
votes
2
answers
549
views
Is density estimation the same as parameter estimation?
I was studying parameter estimation from Sheldon Ross' probability and statistics book. Here the task of parameter estimation is described as follows:
Is this task the same of density estimation in ...
- 151
5
votes
1
answer
2k
views
Is it possible to use variational autoencoders with Non-Gaussian data?
I am dealing with two scenarios: 1) Non-Gaussian data distribution and 2) non-stationary data).
First, I am planning to use a variational autoencoder for modeling the probability distribution of the ...
- 333
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
4
votes
1
answer
1k
views
Can someone explain why neural networks are highly parameterized?
I understand that neural networks by definition, are a parametric model.
If I am correct, Parametric methods make an assumption about the functional form, or shape, of f. For a neural network, what ...
- 71
4
votes
3
answers
479
views
Perfect Prediction: Why Would We Ever Use a Statistical Model?
Dear statistics experts
I need your help with something that has bothered me for a while now. My problem revolves around perfect prediction and essentially boils down to:
Why would we ever set up and ...
- 69
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