Formulate likelihood problems and solve them with maximum likelihood estimation (MLE)
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Updated
Oct 8, 2020 - Julia
Formulate likelihood problems and solve them with maximum likelihood estimation (MLE)
Adjust Poisson and Binomial Generalised Linear Models to their quasi equivalents for dispersed data
Confidence bands for simultaneous statistical inference
Pure julia implementation of Multiple Affine Invariant Sampling for efficient Approximate Bayesian Computation
Approximate Bayesian Computation (ABC) with differential evolution (de) moves and model evidence (Z) estimates.
Julia package which implements and explores the Likelihood-based Profile Wise Analysis workflow for uncertainty quantification
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