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I've been trying to familiarize myself with the Wasserstein distance and saw this answer on StackExchange by @antike that at first made a lot of sense, but then it didn't (to me, of course).
In the ...
I am curious as to how logistic regression handles string variables in a training matched data set
I am aware many use Logistic regression to categorize data that includes the process of matching ...
Background
Compositional data ($x_i>0, \sum_i x_i=c$) are usually analyzed using some kind of log-transformation (alr/clr/ilr), to take into account naturally the fact that, in presence of the sum ...
I learned that statistical distance between two 1-dim distributions F and G
$D_E(F,G)=\int(F(x)-G(x))^2dx$ is famous.
But what about $D(F,G)=\int(F(x)-G(x))^2w(x)dx$ or $D(F,G)=\int(F(x)-G(x))^2w(x)dF(...
Is there a statistical distance between two 1-dim distribution F and G that
$D(F,G)=\int(F(x)-G(x))^2dF(x)$?
Or to symmetrize it, take $D^s(F,G)=\int(F(x)-G(x))^2dF(x)+dG(x)$
If not, why? (What are ...
I have 10x10 distance matrix where the distance metrics is (1 -
overlap coefficient).
I want to represent the observations in this matrix in a low dimensional space to
see how observations relate to ...
The Jensen-Shannon divergence (JSD) measures the (dis)similarity between multiple probability distributions.
How can one determine whether the JSD of (a pair of, or multiple) distributions is ...
for my undergraduate research project, I'm looking for an R code for agglomerative clustering. Basically, I need to know what happened inside hclust method in R. I have looked everywhere but don't ...
I have a pair of joint probability distributions. I want to measure their similarity/dissimilarity. If they were single-dimensional probability distributions, then I could measure the KL-divergence or ...
First time poster here, so please let me know if more details are needed! In short, I'm analyzing a dataset that has scores from 98 siblings from 64 families. So some families contributed only one ...
I have collected data about subjects in 20 different variables (all of them are continuous and have been standardized as Z scores). What I would like to do now is to obtain a valid measure of the ...
Previously, I asked the question how to calculate the expected distance to the nearest neighbor molecule in 3-dimensional space. This question was fully answered, which is I ask this related question ...
I'm dealing with a graph theory problem for which I have calculated a series of pair-wise similarity measures (several criteria such as ancestrality, co-occurrence, sentence similarity, etc.) between ...
Let's say I have a strict ranked set of samples.
I only have a similarity measure $s$. I want to evaluate how good this similarity measure is at ranking the examples.
One approach would be to use a ...
I have a image of size of 64*64. I am trying to compute HOG features for the image. I have skimage for my implementation, with the following parameters:
...
I have categorical data which follow a hierarchical structure (in fact they're medical codes). For instance:
C10: Diabetes Mellitus
E00: Senile dementia
E10: Schizophrenia
E2B1: Chronic Depression
G20:...
Starting Point:
I want to calculate the distances between nominal data. Furthermore, I want to see what difference it makes to only use important features (given some feature selection method). So I ...
Let's say that I have two datasets. The target dataset is 1K instances, the "predicted" dataset is 1M instances (but I could downsample).
I can compute the distance between instances.
How do ...
Summary: I want to model in R the relationships between pairwise spatial distances, pairwise temporal distances, and pairwise Jaccard distances, with the goal of predicting the Jaccard distance ...
I need to quantify the distance of a continuous probability distribution $f(x)$ to the uniform distribution on $x \in (-\infty, +\infty)$. Do you know any distance functions that could help here? For ...
I learned about this option of using mahalanobis distance instead of PS to do matching from the matchit() function in R. It seems a more nonparametric approach. Could you state its pros and cons and ...
I have a medical dataset with both boolean variables and continuous variables (e.g. age/BMI). I know that clustering with K-means won't work due to the mixed datatypes. I read that I can use the Gower'...
I have two matrices, $A$ and $B$, each of size $n\times m$, where $n$ is discrete time points, and $m$ are the variables measured (specifically, $n$ are dates and $m$ are investments measured in ...
I am working through some cluster analysis (trying to propose new item types for various clusters). I have data that has both numeric and nominal features. After creating dummy variables for all ...
[reposting with more detail, after previous question was removed due to lack of detail or clarity]
I am working on getting a better understanding of my company's user base. We have distinct ...
I have many probability distributions, I need to compute the amount of overlap between two probability distributions. I don't know the type of distribution since it really depends on the data itself.
...
I'm applying Hausdorff Distance to understand if two datasets are representing the same subset of the space of a particular problem. The point is that I've read the Hausdorff distance computes the ...
I have two agents that both follow a baseline behavioral policy pi(a|s). If I then modify the state-action distribution for the two agents (resulting in two new policies), is there a standard measure ...
I found in a couple of documents (e.g. this) that the Euclidean distance $d(x, y) = \sqrt{\sum_{i = 1}^{n}{(x_i - y_i)^2}}$ can be obtained from correlation coeffcient if $x$ and $y$ are standardised ...
I want to score a number of similarity metrics, i.e. given a function s(x,y) which returns a number that is higher the more similar x and y are. I'm want to objectively score a number of different ...
I have a (euclidean) distance matrix and I want to perform GMM clustering.
I read in another post (gaussian mixture model - approximate a matrix) that I could apply MDS or PCA to this matrix and use ...
I'm trying to figure out if the set is convex, where the maximum difference between cdf(or inverse cdf) of X and a reference distribution Y is smaller than $\epsilon$.
1.
Let $f_X(t)$ denote the cdf ...
When computing an estimate of the distance correlation of random vectors $X$ and $Y$ using paired samples of the vectors, should the samples be i.i.d? Can I have data in which $X_i$ and $X_j$ are ...
I have a distribution $D$ of points in a normed vector space (it's $\mathbb{R}^n$ using the $L_\infty$ norm, but I don't think that matters).
In this particular space, points that are less than a ...
Let $X$ be a real-valued random variable and $n > 0$. Using numerical methods, how can we find the vector $\vec v$ of $n$ real numbers that is most characteristic of $X$, in the sense that the ...
I've found some survey data where respondents answer 63 question by giving a response for each question between 0-10 (0 for strong disagreement, 10 for strong agreement). So I can view every ...
I am trying to get something like a confusion matrix for different classes, but without training a model.
The idea is to use some kind of distance between classes.
The data set is like this, just for ...
Is there a way to interpret the Jensen-Shannon divergence which is normalized to be between 0 and 1 between two probability distributions as a "percent difference", i.e., there is a x% ...
Clustering involves using some distance or similarity metric.
What is the best way to score the similarity of these small sets of words? Criteria: These are technical terms which are extracted from ...
I want to compare two histograms by using the $\chi^2$-distance. There is a definition in the OpenCV-library. Also this question gives a lot of insight about this distance-metric. Because i didn't ...
I need to be able to select first N most representative points from each cluster calculated by K-means. To do so, I am aiming to calulate the distance of each point to its own cluster center and take ...
Given a symmetric, positive semidefinite matrix A, and matrix D, where $D_{ij}=A_{ii}-2A_{ij}+A_{jj}$, prove that there exist n vectors {$\vec{v_1},...,\vec{v_n}$} such that $D_{ij}=||\vec{v_i}-\vec{...
Suppose $f$ and $g$ are two probability density functions. I have seen economists use $\int f(x)g(x) dx$ as some kind of similarity measure. For example, Jaffe (1986) uses sum of product of two ...
Is there a better measure than Euclidean distance for my problem?
I have two functions e() and f(x). x is a vector of continuous values. The process e() outputs a variable z. Subsequently, a variable ...
Suppose I have a small number of samples drawn from an unknown distribution $\{X_1,X_2,...,X_n\}$, where $0\le X_i \le L$, and $3\le n \le10$. I want to identify a metric to understand how far these ...
Let's say one has a finite collection of i.i.d. samples from an unknown source distribution $S=\{x_{i} | i \in [1,n_{S}], x_{i} \sim p_{X_{S}}(x)\}$. Where each $x$ is multidimensional and has ...
I am trying to analyze a time series of ecological data. Each time point in the series is a matrix of animals-by-foods (that they were observed to eat). For each of these time points, I compute the ...
I am looking for the best way to approximate the Mahalanobis distance by the standardized Euclidean distance, which would reduce the number of the required multiplications. The easiest way is the ...
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