42 skills found · Page 1 of 2
python-windrose / WindroseA Python Matplotlib, Numpy library to manage wind data, draw windrose (also known as a polar rose plot), draw probability density function and fit Weibull distribution
zfit / ZfitModel manipulation and fitting library based on TensorFlow and optimised for simple and direct manipulation of probability density functions. Its main focus is on scalability, parallelisation and user friendly experience.
xxxnell / FlexProbabilistic deep learning for data streams.
ahmedaq / Making Elegant Matlab FiguresA repository comprising multiple functions for making elegant publication-quality figures in MATLAB
olafmersmann / TruncnormDensity, probability, quantile and random number generation functions for the truncated normal distribution.
PavanAnanthSharma / Breeden Litzenberger Formula For Risk Neutral DensitiesThe Breeden-Litzenberger formula, proposed by Douglas T. Breeden and Robert H. Litzenberger in 1978, is a method used to extract the implied risk-neutral probability density function from observed option prices
bearloga / TinydensRA set of RStudio add-ins for playing with distribution parameters and visualizing the resulting probability density and mass functions.
aziele / Statistical DistanceMeasures of distance between two probability density functions
MikeJaredS / HermiterEfficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)
tomicapretto / Density EstimationThis repository contains notebooks with different probability density function estimators.
ratwolfzero / Hopalong PythonGenerative Density Approximation for Deterministic Chaos The Hopalong Attractor
XsarfrazX / CDF PDF MatlabPlotting and analysing Cumulative Distribution Function(CDF) and Probability Density Function(PDF) of Uniform and Gaussian Distribution
mohitkumarahuja / Visual Tracking Using MeanShiftMean-Shift (MS) Mean-Shift (MS) is widely known as one of the most basic yet powerful tracking algorithms. Mean- Shift considers feature space as an empirical probability density function (pdf). If the input is a set of points then MS considers them as sampled from the underlying pdf. If dense regions (or clusters) are present in the feature space, then they correspond to the local maxima of the pdf. For each data point, MS associates it with the nearby peak of the pdf As an example, you can see the car sequence in file “Mean_Shift_Tracking.m”. We want to track the car in this sequence. We first needed to define the initial patch of the car in the first frame of the sequence. And then the moving car patch will be estimated by using the Bhattacharya coefficient and the weights corresponding to the neighboring patches. It will be deeply explained in the report.
royhzq / BetajsAn implementation of the beta distribution probability density function in Javascript. This implementation overcomes the problem of large numbers being generated by the Beta function which can cause JS to return inf values.
arasgungore / Central Limit TheoremA MATLAB project which applies the central limit theorem on PDFs and CDFs of different probability distributions.
bhuyanamit986 / Exploratory Data AnalysisHere I did EDA on the iris dataset using histograms, scatterplots, probability density function(PDF), cumulative distribution function(CDF), box plots, whisker ports
malikfahad / Particle FilterParticle filters or Sequential Monte Carlo (SMC) methods are a set of genetic, Monte Carlo algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made, and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of some Markov process, given some noisy and partial observations. Particle filters implement the prediction-updating transitions of the filtering equation directly by using a genetic type mutation-selection particle algorithm. The samples from the distribution are represented by a set of particles; each particle has a likelihood weight assigned to it that represents the probability of that particle being sampled from the probability density function.
Mottl / LongtailLongtail transforms RV from the given empirical distribution to the standard normal distribution
learn-co-curriculum / Dsc Probability Density FunctionNo description available
PauloCampana / Random VariableRNG, density, probability, survival and quantile functions for various distributions.
