Questions tagged [density-estimation]

The construction of an estimate, based on observed data, of an unobservable underlying probability density function (pdf).

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Use OpenNN for score matching

I‘m new to this topic and need some guidance. I would like to implement score matching (like sliced-score matching: in C++. OpenNN ( seems to ...
0xbadf00d's user avatar
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Packages for Density Estimation using K-Nearest Neighbor

I would like to have suggestions for packages that provide K-Nearest Neighbor density estimator, I've already searched the web (to not bother you guys with my question :) ), but most results were ...
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Connecting timeseries quantities to CDF

In the following paper, ...
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Function for KDE-style distribution generation for sampling

I have some points in pytorch and I would like to sample from a distribution that resembles these points. I noticed that the seaborn kde plots seem to draw out/define a distribution graphically and I ...
nighthawk's user avatar
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logistic regression or density estimation for binary dependent variable and binary (or categorical) features [closed]

I have a binary dependent variable $t$ and categorical features. We can even simplify to binary features since I can one-hot encode the categorical variables. In practice the one-hot encoding induces ...
andins's user avatar
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Why exactly KNN is outperforming Parzen by a huge margin in classificaton task

I'm trying to implement a Naive Bayes classifier, which uses either of hypercubic Parzen window or KNN to estimate a density function. The data I'm using is Fashion MNIST. The steps I take are that ...
Farhood ET's user avatar
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MLE for Poisson conditioned on multivariate Gaussian?

I am writing some Python code to fit 2D Gaussians to fluorescent emitters on a dark background to determine the subpixel-resolution (x, y) position of the fluorescent emitter. The crude, pixel-...
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How to evaluate KDE against histogram?

I am currently testing some approaches for density estimation, and I think the basic approach of histograms may not be the best option to me and KDE is certainly a good alternative to go. While ago I ...
Adelson Araújo's user avatar
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Learn smoothly varying mean and variance of a variable over a 2d domain

For a problem which I am working on at the moment, I'm interested in learning how the mean and variance of some response variable y changes with two independent ...
A. White's user avatar
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How to convert regression into classification?

So I have a regression problem with bunch of features X, and labels in the amount (price $). How can I convert it to classification problem? I have read about convert label from continuous to ...
Nauman Akram's user avatar
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how can i interpret kernel density plots from classification?

all, i have a classification problem where i am predicting likelihood of client defaulting on loan. i plotted the predicted probabilities from my model, and then plotted against the label '1' for ...
Maths12's user avatar
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Test independence based on Kernel Density Estimation

I am working on a problem where I have a dataset of $X$ is dataset with $(X, Y, T, K)$ four attributes, I'd like to test if $P(X, Y, T)P(K) = P(X, Y, T, K)$, that is if $X, Y, T$ is independent of $K$....
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Simple example of Parzen window (kernel density estimation)

I am confused about the Parzen Window question. Suppose we have two training data points located at 0.5 and 0.7, and we use 0.3 as its rectangle window width. How do we estimate its probability ...
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