There are 0 repository under likelihood topic.
Model 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.
R package for statistical inference using partially observed Markov processes
:no_entry_sign: :leftwards_arrow_with_hook: A document that introduces Bayesian data analysis.
Maximum Likelihood estimation and Simulation for Stochastic Differential Equations (Diffusions)
Implementation of the conjugate prior table for Bayesian Statistics
Provides likelihood functions for Gaussian Processes.
Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign
LikelihoodProfiler is a Julia package for practical identifiability analysis and confidence intervals estimation.
HuMoLiRe is a Pedestrian Dead-Reckoning Particle Filter Map-aided system that leverages human motion likelihood in indoor spaces to estimate their position. This repository is the dataset and software published with its paper.
Methods for profile likelihood analysis.
GUNDAM, for Generalized and Unified Neutrino Data Analysis Methods, is a suite of applications which aims at performing various statistical analysis with different purposes and setups.
Official project of DiverseSampling (ACMMM2022 Paper)
package useful for likelihood-based inference
Interface for mathematical/statistical densities in Julia
Generate target statistics
An introduction to some generalized linear models using the likelihood approach and R. (scroll down for a menu)
pomp in Julia
A cobaya low-ell likelihood polarized for planck 2020 data (NPIPE release)
Non-parametric conditional density estimation with binned conditional histograms
Resurrecting BAMBI for the pythonic deep learning era
This repository contains the code used to perform the analysis described in the paper "A stacked search for spatial coincidences between IceCube neutrinos and radio pulsars" (https://arxiv.org/abs/2306.03427). The code is written in Python 3.10 and uses the following packages: numpy, scipy, matplotlib, pandas, numba, multiprocessing.
Replication package for Abbring and Salimans (2021), "The Likelihood of Mixed Hitting Times," with MATLAB code for estimating mixed hitting-time models
A library of methods to mitigate experimenter bias in the LSST DESC cosmology analysis
Likelihood-Based Inference for Time Series Extremes
A cobaya high-ell likelihood polarized for planck 2020 data (NPIPE release)