Cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation

Astronomy and Computing

Volumn 13, Issue , 2015, Pages 1-11

  Ishida, E E O ^a   Vitenti, S D P ^b   Penna Lima, M ^c,d   Cisewski, J ^e   de Souza, R S ^f   Trindade, A M M ^g,h   Cameron, E ⁱ   Busti, V C ^j,k  

^a MAX PLANCK INSTITUT FÜR ASTROPHYSIK   (Germany)

^b INSTITUT D'ASTROPHYSIQUE DE PARIS   (France)

^c UNIVERSITÉ PARIS DIDEROT   (France)

^d INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

^e YALE UNIVERSITY   (United States)

^f EÖTVÖS LORÁND UNIVERSITY   (Hungary)

^g CENTRO DE ASTROFÍSICA   (Portugal)

^h UNIVERSITY OF PORTO   (Portugal)

ⁱ UNIVERSITY OF OXFORD   (United Kingdom)

^j UNIVERSITY OF CAPE TOWN   (South Africa)

^k UNIVERSITY OF SÃO PAULO   (Brazil)

more..

Author keywords

(cosmology:) large scale structure of universe; Galaxies: statistics

Indexed keywords

BAYESIAN NETWORKS; GALAXIES; IMPORTANCE SAMPLING; MONTE CARLO METHODS; PROBABILITY DISTRIBUTIONS; SAMARIUM COMPOUNDS; SAMPLING;

ADAPTIVE IMPORTANCE SAMPLING; APPROXIMATE BAYESIAN; COMPLEX PHYSICAL SYSTEMS; COSMOLOGICAL PARAMETERS; GALAXIES: STATISTICS; LARGE SCALE STRUCTURE OF UNIVERSE; LIKELIHOOD FUNCTIONS; POPULATION MONTE CARLO;

PARAMETER ESTIMATION;

EID: 84943150900     PISSN: 22131337     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ascom.2015.09.001     Document Type: Article

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