2019
DOI: 10.1093/mnras/stz2718
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Primordial power spectrum and cosmology from black-box galaxy surveys

Abstract: We propose a new, likelihood-free approach to inferring the primordial matter power spectrum and cosmological parameters from arbitrarily complex forward models of galaxy surveys where all relevant statistics can be determined from numerical simulations, i.e. black-boxes. Our approach, which we call simulator expansion for likelihood-free inference (selfi), builds upon approximate Bayesian computation using a novel effective likelihood, and upon the linearisation of black-box models around an expansion point. … Show more

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Cited by 22 publications
(17 citation statements)
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“…Jennings & Madigan 2017;Herbel et al 2017;Hahn et al 2017;List & Lewis 2020;Leclercq et al 2019;Alsing et al 2019; Kacprzak et al 2 In the rest of this paper, we will follow recent convention and use the term SBI.MNRAS 000,1-19 (2021) …”
mentioning
confidence: 99%
“…Jennings & Madigan 2017;Herbel et al 2017;Hahn et al 2017;List & Lewis 2020;Leclercq et al 2019;Alsing et al 2019; Kacprzak et al 2 In the rest of this paper, we will follow recent convention and use the term SBI.MNRAS 000,1-19 (2021) …”
mentioning
confidence: 99%
“…Moreover, if we want to use a more sophisticated sampler such as Hamiltonian Monte Carlo (HMC), one can leverage the gradients from the emulator to derive an expression for the gradient of the negative loglikelihood (the potential energy function in an HMC scheme) with respect to the input cosmological parameters, under the assumption that such an analytic derivation is possible. Furthermore, the second derivatives can be used in a Fisher Matrix analysis, or the first and second derivatives can be be used together in an approximate inference scheme based on Taylor expansion techniques, see for example, the recent work by Leclercq et al (2019). In addition, similar concepts behind this work can be extended to build emulators for P δ (k, z) from Nbody simulations.…”
Section: Discussionmentioning
confidence: 99%
“…In the same spectrum, Alsing et al (2018) developed a density-estimation likelihood-free method and argued that it requires less tuning compared to traditional approaches such as Approximate Bayesian Computation, ABC. Leclercq et al (2019) also developed a likelihood-free approach to infer power spectrum and cosmological parameters from forward simulations only.…”
Section: Introductionmentioning
confidence: 99%