All Questions
6
questions
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
1
vote
0
answers
135
views
Extraction of modes from a multi-modal density function
I am trying to extract modes from a multi-modal density function and not just peaks. For example, in the two density functions below (images), I would like to extract the curves contained in the black ...
- 323
2
votes
1
answer
39
views
Why might the functional form of a distribution be "inappropriate" for a particular application?
Working through Bishop's Pattern Recognition and Machine Learning(a great read so far!) and on page 67 he says:
"One limitation of the parametric approach is that it assumes a specific ...
- 1,617
4
votes
0
answers
442
views
Derivation of k nearest neighbor classification rule
One way to derive the k-NN decision rule based on the k-NN density estimation goes as follows:
given $k$ the number of neighbors, $k_i$ the number of neighbors of class $i$ in the bucket, $N$ the ...
- 426
1
vote
1
answer
353
views
How Parzen window density estimate $f_n$ converges to f
I am trying to understand how Parzen window density estimate converges to actual density function f(x).[Actually i am trying to learn machine learning on my own using available free resources. Please ...
2
votes
1
answer
381
views
Learn a distribution from distributions on samples [closed]
There's many good ways to learn a distribution $p_X$ of an r.v. $X$ over $k$ symbols given many i.i.d. samples $X_1,\ldots, X_n$. The simplest is to use the sample relative frequencies $\hat{f}_X$ as ...
- 421