A simulation-based inference framework that jointly models type Ia supernovae brightness dependences, host galaxy evolution, and cosmology from photometric observations.
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Exact marginalization over binary variables in data models maps onto the Ising model, enabling efficient likelihood calculations and approximations demonstrated on Type Ia supernova calibration.
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CIGaRS I: Combined simulation-based inference from type Ia supernovae and host photometry
A simulation-based inference framework that jointly models type Ia supernovae brightness dependences, host galaxy evolution, and cosmology from photometric observations.
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Analytic Marginalization over Binary Variables in Physics Data
Exact marginalization over binary variables in data models maps onto the Ising model, enabling efficient likelihood calculations and approximations demonstrated on Type Ia supernova calibration.