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Semantic Scholar Open Access 2020 309 sitasi

sbi: A toolkit for simulation-based inference

Álvaro Tejero-Cantero Jan Boelts Michael Deistler Jan-Matthis Lueckmann Conor Durkan +21 lainnya
Lihat Sumber DOI

Abstrak

e Equally contributing authors 1 Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich 2 School of Informatics, University of Edinburgh 3 Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn 4 Model-Driven Machine Learning, Centre for Materials and Coastal Research, Helmholtz-Zentrum Geesthacht 5 Machine Learning in Science, University of Tübingen 6 Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen DOI: 10.21105/joss.02505

Topik & Kata Kunci

Computer Science

Penulis (26)

Á

Álvaro Tejero-Cantero

J

Jan Boelts

M

Michael Deistler

J

Jan-Matthis Lueckmann

C

Conor Durkan

P

Pedro J. Gonccalves

D

David S. Greenberg

J

Jakob H. Macke Computational Neuroengineering

D

D. Electrical

C

Computer Engineering

T

T. U. Munich

S

School of Informatics

U

U. Edinburgh

N

Neural Systems Analysis

C

Center of Advanced European Studies

R

Research

B

Bonn

M

Model-Driven Machine Learning

C

Centre for Materials

C

Coastal Research

H

Helmholtz-Zentrum Geesthacht

M

Machine Learning in Science

U

U. Tubingen

E

Empirical Inference

M

Max Planck Institute for the Physics of Complex Systems

T

Tubingen

Format Sitasi

APA MLA BibTeX
Tejero-Cantero, Á., Boelts, J., Deistler, M., Lueckmann, J., Durkan, C., Gonccalves, P.J. et al. (2020). sbi: A toolkit for simulation-based inference. https://doi.org/10.21105/joss.02505

Akses Cepat

Lihat di Sumber doi.org/10.21105/joss.02505
Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Total Sitasi
309×
Sumber Database
Semantic Scholar
DOI
10.21105/joss.02505
Akses
Open Access ✓