Golang implementation of the OpenTelemetry auto-scaling base-2-exponential histogram

Collection of probabilistic data structures - CM-Sketch, ECM-Sketch, exponential histogram

Exponential histograms is a data structure for sliding windows. It is from `Maintaining Stream Statistics over Sliding Windows, M.Datar, A.Gionis, P.Indyk, R.Motwani; ACM-SIAM, 2002`.

Collection of probabilistic data structures - CM-Sketch, ECM-Sketch, exponential histogram in C++.

A repository which contains codes for various contrast enhancement techniques like log transform, exponential transform, power-law transform, histogram sliding, histogram stretching and histogram equalization

Responsibility Included: 1) Change the display levels using linear, logarithmic , exponential and broken linear transfer curves. 2) Transform the image with histogram equalization using two methods: overall (global) equalization and local equalization. 3) Create own image equalization method (combination of exponential transfer curve) allow user to see contrast of radio-graph image.

In this Analysis you can see the health of all people who smoke in world. There is also full detailed info for this and every have every graph (e.g pie chart, Bar Graph, Line Graph, Histogram, Exponential Graphs, Logarithmic Graphs etc). You can aslo take help from these analysis

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Linear, piece wise linear, logarithmic, exponential stretching, power law transformation, histogram equalization, Adaptive HE, BBHE, DSIHE and CLAHE for CPU using C and OpenCV 2.4.13

Utilized SciPy’s stats module to fit six probability distributions to maximum yearly discharge data of four different sites. Checked applicability of Gaussian, Exponential, Gamma, Lognormal distributions and plotted histograms.

Implemented anonymization and differential privacy techniques in Python. Key tasks include constructing ε-DP histograms, applying LDP protocols (GRR, RAPPOR, OUE), and using the Exponential Mechanism. Conducted experiments to evaluate privacy-accuracy trade-offs using real datasets.

This exercise performs 10,000 trials drawing N samples from independent exponential and uniform random variables. For N = 2, 20, 200, 500, the sample means and variances are computed, histograms plotted, and standard deviations of their distributions analyzed and compared.

a web page (running on local host) with a button which when clicked would display a histogram with the following specifications. • The data for the histogram should be a random sample of 20 numbers taken without replacement from {1, 2, 3, . . . , 100} • The each block of histogram should be a shade of blue. • A block of histogram should turn orange when the mouse is placed on it. • When the mouse is rolled over a subset of adjacent blocks then they should appear in shades of orange.Further customization can b e done by asking visitors to the web p age for the following inputs and creating the histogram accordingly. • sample size • ◦ with replacement or ◦ without replacement • Normal with µ = and σ = • Uniform with minimum = and maximum = • Exponential with λ =

Sequential Exponential Histograms for TimescaleDB

Runtime estimation of arrival processes using Bernstein exponential distributions and sliding-window histograms.

Exploration of the error produced by different ways of calcuating Exponential Histogram Bins

Analysis ToolPak, Histogram, Descriptive Statistics, Moving Average, Exponential Smoothing, Correlation Analysis, Regression Analysis

R Shiny application analyzing compound stochastic processes S(t) with exponential interarrival times and exponential claim sizes. Features interactive histograms at t=10,100,1000,10000 and parameter sensitivity analysis.

The following code generates random values of an exponential distribution and plots them as a histogram.

Streamlit app for best-fit probability analysis. Upload datasets or histograms to fit Normal, Lognormal, Weibull, Gamma (2P/3P), and Exponential distributions. Displays AIC, BIC, KS stats, percentiles, and interactive Plotly plots. Supports dataset export and histogram-based data generation.

This repository includes R code that models a compound Poisson process with exponential arrivals and exponential jumps. It generates histograms for different time values and provides a Shiny app to explore how changing parameters affects 𝑆 ( 𝑡 ). A simple and interactive way to understand compound processes.

Generates random variables for exponential & uniform distributions. Also, plots histogram, calculating expected value & comparing the result. In addition computes & plots cumulative distributions.

This project implements Huffman Coding to compress randomly generated sequences of letters using both uniform and heavy-tail (power-law, exponential) probability distributions. The program calculates entropy, visualizes histograms, and compares Huffman encoding efficiency with theoretical entropy.

Streamlit Curve Fitter App will take any data set of x-y values and plot the best curve with its equation onto a graph. Curves in this program include polynomials, exponential, logarithmic, trigonometric, statistical distributions (Gaussian), and histograms.

This project generates and analyzes exponential and normal distributions using Python. It computes key statistics (mean, variance, skewness) and visualizes data through histograms with theoretical curves. Results are exported to CSV for further analysis, aiding applications in finance, engineering, and risk assessment.

A full R Shiny application that simulates the compound Poisson process 𝑆 ( 𝑡 ) = ∑ 𝑖 = 1 𝑁 ( 𝑡 ) 𝑋 𝑖 , where interarrival times and jump sizes are exponential. Includes derivation (MGF, Laplace transform, pdf), four-panel histograms for multiple time horizons, theoretical overlays, and sensitivity analysis for parameters 𝜆 and 𝜇.

This MATLAB-based GUI application allows users to apply various linear and non-linear filters to images, generate different types of noise (salt & pepper, Gaussian, Rayleigh, Exponential, Gamma, etc.), and visualize the results with histogram comparisons.

Monte Carlo Radiation Shielding Simulator with Streamlit UI. Implements photon transport physics using Klein–Nishina scattering and exponential free‑path sampling. Provides interactive modes for single thickness analysis and HVL extraction, with energy spectrum, depth histogram, and transmission curve visualization.

A Streamlit-based tool for curve fitting on user-provided data. Users can manually input or upload CSV data, then fit various curves (Linear, Polynomial, Exponential, Gaussian, Histogram). Visualizations, equations, and fit quality (MAE, R-squared) are displayed. Fitted data and plots can be downloaded.