Topic: "likelihood-functions"
JuliaGaussianProcesses/GPLikelihoods.jl
Provides likelihood functions for Gaussian Processes.
Language: Julia - Size: 751 KB - Last synced at: 18 days ago - Pushed at: 11 months ago - Stars: 43 - Forks: 5
xpsi-group/xpsi
X-PSI: X-ray Pulse Simulation and Inference
Language: Python - Size: 514 MB - Last synced at: about 11 hours ago - Pushed at: 11 days ago - Stars: 40 - Forks: 21
LSSTDESC/firecrown
DESC Cosmology Likelihood Framework
Language: Python - Size: 20.6 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 30 - Forks: 8
JuBiotech/calibr8
Toolbox for non-linear calibration modeling.
Language: Jupyter Notebook - Size: 8.56 MB - Last synced at: 12 days ago - Pushed at: 7 months ago - Stars: 24 - Forks: 1
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: TeX - Size: 72.5 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 22 - Forks: 3
lbelzile/mev
Modelling extreme values
Language: R - Size: 59.4 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 15 - Forks: 3
rplzzz/mcpar 📦
Parallel Metropolis-Hastings Markov chain Monte Carlo toolkit
Language: C++ - Size: 54.7 KB - Last synced at: about 1 year ago - Pushed at: over 8 years ago - Stars: 4 - Forks: 1
austinschneider/MCLLH
Likelihood to account for Monte Carlo statistical uncertainties
Language: CSS - Size: 438 KB - Last synced at: 7 months ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 1
faezehabibi66/MLE-Maximum-Likelihood-Estimation
Estimation Boston Housing using Maximum Likelihood Estimation
Language: Python - Size: 47.9 KB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0
bcbi/MaximumLikelihoodProblems.jl
Formulate likelihood problems and solve them with maximum likelihood estimation (MLE)
Language: Julia - Size: 276 KB - Last synced at: 3 months ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0
ameli/detkit
Matrix Determinant Toolkit (memdet)
Language: Python - Size: 24.7 MB - Last synced at: 6 days ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0
jaspervrugt/DREAM-Suite
DiffeRential Evolution Adaptive Metropolis algorithm: MATLAB and Python Toolbox
Language: MATLAB - Size: 27.6 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0
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: HTML - Size: 3.72 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
queelius/dfr_dist
Dynamic failure rate distributions (DFR)
Language: R - Size: 418 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
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: R - Size: 68.4 KB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0
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 Notebook - Size: 385 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0
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 Notebook - Size: 388 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0