Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions

By Chad M. Schafer, P. Freeman. 2012

32 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings (2015, methodology): “Originated in population genetics (Beaumont et al., 2002), ABC has been widely used in a broad range of scientific applications including ecology, cosmology, and bioinformatics (Ratmann et al., 2007; Bazin et al., 2010; Schafer & Freeman, 2012).”
Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation (2016, background): “Simulator-based modelling lends itself naturally to scientific domains such as evolutionary biology [1], ecology [24], disease epidemics [10], economics [8] and cosmology [23], where observations are best understood as products of underlying physical processes.”
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation (2014, background): “bution of parameter settings that provide accurate simulation results [20, 3, 18].”
Nuisance hardened data compression for fast likelihood-free inference (2019, methodology): “…forward simulations only, incorporating exactly all known effects that can be successfully simulated without relying on approximate likelihoods (Schafer & Freeman 2012; Cameron & Pettitt 2012; Weyant et al. 2013; Robin et al. 2014; Lin & Kilbinger 2015; Ishida et al. 2015; Akeret et al. 2015;…”
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows (2018, background): “…naturally to modelling mechanistic processes such as particle collisions in high energy physics [1, 71], universe evolution in cosmology [2, 67], stochastic dynamical systems in ecology and evolutionary biology [64, 65, 84, 85], macroeconomic models in econometrics [3, 26], and biological…”
Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology (2018, methodology): “…2016 for applications in the astronomy literature). abc methods have been applied successfully to a number of problems in cosmological data analysis (Schafer & Freeman 2012; Cameron & Pettitt 2012; Weyant et al. 2013; Robin et al. 2014; Lin & Kilbinger 2015; Hahn et al. 2017; Kacprzak et al.…”