likelihood-functions

There are 15 repositories under likelihood-functions topic.

  • JuliaGaussianProcesses/GPLikelihoods.jl

    Provides likelihood functions for Gaussian Processes.

    Language:Julia435345

  • xpsi-group/xpsi

    X-PSI: X-ray Pulse Simulation and Inference

    Language:Python34321421

  • LSSTDESC/firecrown

    DESC Cosmology Likelihood Framework

    Language:Python29701987

  • JuBiotech/calibr8

    Toolbox for non-linear calibration modeling.

    Language:Jupyter Notebook243211
  • concurve

    zadrafi/concurve

    A repository for the 'concurve' R package which generates confidence distributions and likelihood functions. Includes documentation on how to do produce similar graphs for Stata.

    Language:TeX20343

  • lbelzile/mev

    Modelling extreme values

    Language:R123184

  • rplzzz/mcpar

    Parallel Metropolis-Hastings Markov chain Monte Carlo toolkit

    Language:C++4211

  • austinschneider/MCLLH

    Likelihood to account for Monte Carlo statistical uncertainties

    Language:CSS2301

  • bcbi/MaximumLikelihoodProblems.jl

    Formulate likelihood problems and solve them with maximum likelihood estimation (MLE)

    Language:Julia179

  • ameli/detkit

    Matrix Determinant Toolkit (memdet)

    Language:Python0200

  • Scrayil/GlobClus_prop-Analysis

    This aim of this project is to analyze globular star clusters in the Milky Way, in order to understand their dynamics. The conducted study examined the properties that affect the central velocity dispersion, their impact and the correlations between them.

    Language:HTML0100

  • ashishyadav24092000/MaximumLikelihoodEstimator

    The maximum likelihoood estimator approach is used here for calculating the Regression parameter that is slope(b1),intercept(b0) and standard deviation of error/residuals. Then Result or the output for the regression parameters using the OLS(ordiniary Least Sqaure) estimation method versus the MLE(MAximum Likelihood Estimation) method is compared. Also note that this MLE is used when the residual(e) of the regression model does not follow normal distribution for different observations.

    Language:Jupyter Notebook10

  • ashishyadav24092000/MAximumLIkelihoodEstimator2_TVads_and_carsold

    Here for a small dataset we have used OLS(Ordiniary Least Square) and MLE(Maximum likelihood Estimation ) to calculate the regression parameters slope(b1),intercept(b0) and standard deviation of reisduals.At the end we can conclude that both the methods of estimation produces the same result.

    Language:Jupyter Notebook10

  • PeterSchuld/TUe-Improving_Statistical_Inferences

    Eindhoven University of Technology (TU/e) course "Improving your statistical inferences" by Daniel Lakens on Coursera (completed Dec 2022).

    Language:R20

  • queelius/dfr_dist

    Dynamic failure rate distributions (DFR)

    Language:R10