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Hall and Taylor, 2018 - A Bayesian method for combining theoretical and simulated covariance matrices for large-scale structure surveys
Heavens et al., 2017 - Massive data compression for parameter-dependent covariance matrices
Friedrich1 and Eifler, 2017 - Precision matrix expansion – efficient use of numerical simulations in estimating errors on cosmological parameters
Seletin and Heavens, 2016 - Quantifying lost information due to covariance matrix estimation in parameter inference
Seletin and Heavens, 2015 - Parameter inference with estimated covariance matrices
Taylor and Joachimi, 2014 - Estimating Cosmological Parameter Covariance
Dodelson and Schneider, 2013 - The Effect of Covariance Estimator Error on Cosmological Parameter Constraints
Taylor et al., 2012 - Putting the Precision in Precision Cosmology: How accurate should your data covariance matrix be?
Hartlap et al., 2006 - Why your model parameter confidences might be too optimistic – unbiased estimation of the inverse covariance matrix
Fan et al., 2016 - Approaches to High-Dimensional Covariance and Precision Matrix Estimation
Fan et al., 2015 - An Overview on the Estimation of Large Covariance and Precision Matrices
Ollerer and Croux, 2015 - Robust high-dimensional precision matrix estimation
Fish movement modelling - A Statistical Model for Estimation of Fish Density Including Correlation in Size, Space, Time and between Species from Research Survey Data
Seletin and Heavens, 2018 - On the insufficiency of arbitrarily precise covariance matrices: non-Gaussian weak lensing likelihoods
Witzel et al., 2018 - Variability Timescale and Spectral Index of Sgr A in the Near Infrared: Approximate Bayesian Computation Analysis of the Variability of the Closest Supermassive Black Hole*
Hsu et al., 2018 - Improving the accuracy of planet occurrence rates from Kepler using Approximate Bayesian Computation
Alsing et al., 2018 - Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
Herbel et al., 2017 - The redshift distribution of cosmological samples: a forward modeling approach
Davies et al., 2017 - A new method to measure the post-reionization ionizing background from the joint distribution of lyman-α and lyman-β forest transmission
Kacprzak et al., 2017 - Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions
Peel et al., 2016 - Cosmological constraints with weak lensing peak counts and second-order statistics in a large-field survey
Jennings et al., 2016 - A new approach for obtaining cosmological constraints from type Ia supernovae using Approximate Bayesian Computation
Jennings et al., 2016 - astroABC: An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation
Hahn et al., 2016 - Approximate Bayesian Computation in Large Scale Structure: constraining the galaxy-halo connection
Lin et al., 2016 - A new model to predict weak-lensing peak counts - III. Filtering technique comparisons
Bovy, 2015 - The chemical homogeneity of open clusters
Lin and Kilbinger, 2015 - A new model to predict weak-lensing peak counts - II. Parameter constraint strategies
Akeret et al.. 2015 - Approximate Bayesian Computation for Forward Modeling in Cosmology
Ishida et al., 2015 - cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
Weyant et al., 2013 - Likelihood-Free Cosmological Inference with Type Ia Supernovae: Approximate Bayesian Computation for a Complete Treatment of Uncertainty
Cameron and Pettitt, 2012 - Approximate Bayesian Computation for Astronomical Model Analysis: A Case Study in Galaxy Demographics and Morphological Transformation at High Redshift
Simola et al., 2018 - Approximate Bayesian Computation for Finite Mixture Models
Izbicki et al., 2018 - ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
Jethava and Dubhashi, 2017 - GANs for LIFE: Generative Adversarial Networks for Likelihood Free Inference
Frazier et al., 2016 - Asymptotic Properties of Approximate Bayesian Computation
Jiang et al., 2015 - Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network
Prangle et al., 2013 - Diagnostic tools for approximate Bayesian Computation using the coverage property
Blum et al., 2012 - A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
Beaumont et al., 2008 - Adaptive approximate Bayesian computation
Chapter 1 - Overview of Approximate Bayesian Computation
Chapter 3 - Regression approaches for Approximate Bayesian Computation
Chapter 4 - ABC Samplers
Chapter 5 - Summary Statistics in Approximate Bayesian Computation
Chapter 7 - ABC and Indirect Inference
Chapter 8 - High-dimensional ABC
Chapter 10 - Approximating the Likelihood in Approximate Bayesian Computation
Appendix - A Guide to General-Purpose Approximate Bayesian Computation Software
Dutta et al., 2017 - ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation - code
Csillery et al., 2011 - abc: an R package for Approximate Bayesian Computation (ABC) - code