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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Using variable bandwidths in a KDE estimate

I'm working with geospatial data that is pretty irregularly spaced, but I have a length scale estimate for the spacing in the samples so in principle I have a "bandwidth" for every data ...
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Machine Learning for conditional density estimation

Suppose I have a set of examples $X = (x_1,x_2,..,x_n)$ with continuous numeric targets $Y = (y_1,y_2,..,y_n)$. While it is standard to use regression models to make point predictions of $y_i$ as $f(...
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KDE Sampling with negative density and/or class-specific weighting

I have a dataset which contains two overlapping distributions/classes of points. I have been trying to sample from just one of these distributions/classes using the scikit learn Kernel Density class, ...
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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 ...
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sklearn.neighbors.KernelDensity - score(X) explanation

For sklearn.neighbors.KernelDensity, its score(X) method according to the sklearn KDE documentation says: Compute the log-...
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Reverse scaling Synthetic KDE data

For Python 3.9, sklearn version: 0.24.2 and numpy version: 1.20.3, I am using a Kernel Density Estimation (KDE) generative model. The goal is to generate new data using a given input data. The steps ...
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Generating new sample with same distribution

I have a timeseries data for 1 week. The data contains readings from a device for certain hours of the day. There are about 8-10 readings per day at different timestamps. The timestamps recorded for ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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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