20.500.12556/RUL-171138 Accelerating astronomical and cosmological inference with preconditioned Monte Carlo We introduce preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial geometry. PMC utilizes a Normalizing Flow (NF) in order to decorrelate the parameters of the distribution and then proceeds by sampling from the preconditioned target distribution using an adaptive Sequential Monte Carlo (SMC) scheme. The results produced by PMC include samples from the posterior distribution and an estimate of the model evidence that can be used for parameter inference and model comparison, respectively. The aforementioned framework has been thoroughly tested in a variety of challenging target distributions achieving state-of-the-art sampling performance. In the cases of primordial feature analysis and gravitational wave inference, PMC is approximately 50 and 25 times faster, respectively, than nested sampling (NS). We found that in higher dimensional applications, the acceleration is even greater. Finally, PMC is directly parallelisable, manifesting linear scaling up to thousands of CPUs. data analysis statistics large-scale structure of the Universe analiza podatkov statistika struktura Vesolja true false true Angleški jezik Slovenski jezik Neznano 2025-08-08 10:28:33 2025-08-08 10:28:34 2025-08-09 04:25:57 0000-00-00 00:00:00 2022 0 0 Str. 1644-1653 iss. 2 Vol. 516 Oct. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-10-01 524.8:536.911 0035-8711 10.1093/mnras/stac2272 243782403 RAZ_Karamanis_Minas_2022.pdf RAZ_Karamanis_Minas_2022.pdf 1 BAE847C0D837A9621DAB20B8DECAF2F2 28165df4f40a9e151f6b3d7b02cdc103b8efdb2cb333f4a4eb9e5bc499763412 593b01a7-7432-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=214058 https://academic.oup.com/mnras/article/516/2/1644/6670808 1 9b869345-7431-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=214057 Fakulteta za računalništvo in informatiko 0 0 0