detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2410.07548
Tema
Statistics - Machine Learning Astrophysics - Cosmology and Nonga... Computer Science - Information The... Computer Science - Machine Learnin... Physics - Data Analysis, Statistic...Autor
Makinen, T. Lucas Sui, Ce Wandelt, Benjamin D. Porqueres, Natalia Heavens, AlanCategoría
ciencias: astrofísica
Año
2024
fecha de cotización
16/10/2024
Palabras clave
inference informationResumen
We present a way to capture high-information posteriors from training sets that are sparsely sampled over the parameter space for robust simulation-based inference.
In physical inference problems, we can often apply domain knowledge to define traditional summary statistics to capture some of the information in a dataset.
We show that augmenting these statistics with neural network outputs to maximise the mutual information improves information extraction compared to neural summaries alone or their concatenation to existing summaries and makes inference robust in settings with low training data.
We introduce 1) two loss formalisms to achieve this and 2) apply the technique to two different cosmological datasets to extract non-Gaussian parameter information.
;Comment: 7 pages, 4 figures.
Accepted to ML4PS2024 at NeurIPS 2024
Makinen, T. Lucas,Sui, Ce,Wandelt, Benjamin D.,Porqueres, Natalia,Heavens, Alan, 2024, Hybrid Summary Statistics
Utility of Photon-Counting Detector CT Myelography for the Detection of CSF-Venous Fistulas
imaging photon-counting myelography
