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both researchers and practitioners. Whether you need fine-grained control or an
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easy-to-use interface, ` sbi ` has you covered.
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- With ` sbi ` , you can perform simulation-based inference (SBI) using a Bayesian approach:
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- Given a simulator that models a real-world process, SBI estimates the full posterior
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+ With ` sbi ` , you can perform parameter inference using Bayesian inference: Given a
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+ simulator that models a real-world process, SBI estimates the full posterior
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distribution over the simulator’s parameters based on observed data. This distribution
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indicates the most likely parameter values while additionally quantifying uncertainty
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and revealing potential interactions between parameters.
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# ` sbi ` : simulation-based inference toolkit
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+ ` sbi ` is a Python package for simulation-based inference, designed to meet the needs of
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+ both researchers and practitioners. Whether you need fine-grained control or an
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+ easy-to-use interface, ` sbi ` has you covered.
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+
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+ With ` sbi ` , you can perform parameter inference using Bayesian inference: Given a
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+ simulator that models a real-world process, SBI estimates the full posterior
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+ distribution over the simulator’s parameters based on observed data. This distribution
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+ indicates the most likely parameter values while additionally quantifying uncertainty
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+ and revealing potential interactions between parameters.
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+
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` sbi ` provides access to simulation-based inference methods via a user-friendly
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interface:
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