fitting-distribution

There are 4 repositories under fitting-distribution topic.

• Lmo

  jorenham / Lmo

  Trimmed L-moments and L-comoments for robust statistics.

  l-momentsnumpypythonstatisticsrobust-optimizationrobust-statisticsfitting-distributionmultivariate-statisticsprobability-statisticsl-comomentstl-momentslmo
  Language:Python 7

• RishiDarkDevil / Numerical_Analysis_Projects

  Numerical Analysis Projects

  simulationphysics-simulationmatrix-factorizationinterpolationmatrix-manipulationcurve-fittingfitting-distributionfinite-element-methodsdifferential-equationsmaximum-likelihood-estimationroot-finding
  Language:HTML 4

• dinhtrung21 / fit-ellipsoid

  Fitting a 3D ellipsoid to an arbitrary 3D shape for RVE evaluation.

  fitting-algorithmfitting-distributionmicrostructure
  Language:Python 2

• tk-yoshimura / DoubleDoubleStatistic

  Double-Double Statistic Implements

  distributionmathprobability-distributionstatisticsdotnet8net8fitting-distributionrandom-generationstable-distributionhigh-precisiondouble-double
  Language:C# 1

• cliffsun / statfit

  Outputs a specified amount of data that corresponds to a user-inputted standard deviation. Currently only supported for data from a symmetric Gaussian Distribution.

  fitting-distributionstatistics
  Language:Python 0

• Henry-Sundland / Python-Fitting-Codes

  Codes I created, in python, to fit to supplied data sets and the like

  analysisdata-analysisdata-sciencefittingfitting-algorithmfitting-curvefitting-distributionmath-modeling
  Language:Python 0

• IA3005 / jmva_paper_code

  Code for fitting EEG data with Wishart and t-Wishart distributions

  eegfitting-distributionkolmogorov-smirnov-testsssvepwishart-distributions
  Language:Python 0
• Fitting-Probability-Distribution

  WDpad753 / Fitting-Probability-Distribution

  When dealing with any type of dataset it is important to create a probabilistic model of the data which would help to understand the dataset. The probabilistic model uses probability distribution which assists in explaining about dataset with the implementation of random processes. The main reason for this blog is that in recent years there has been an increase in the use of probability distributions in different disciplines like engineering, biology, weather forecasting, stocks and investments and many more. Hence, it is very important to fit the theoretical distribution to a custom dataset to find the distribution that could provide information/knowledge about the input data.

  fitting-distributionprobability-distributionpython
  Language:Python