Cornell University researchers have published a study describing a computer algorithm that enables machines to infer stochastic reaction models without human intervention or prior knowledge of the nature of the system being modeled.
Stochastic inference is the process of determining the set of rules, including unpredictable factors, that lead to particular outcomes. This reverses the much simpler stochastic prediction, which uses known rules with uncertain elements to simulate possible outcomes.
The researchers say their algorithm could help uncover elusive laws that govern fields ranging from chemistry to molecular biology. Using intermittent samples, such as the number of prey and predating species in a forest once a year, the algorithm can infer probable reactions that caused the result. The researchers applied the algorithm to microorganisms in a closed ecosystem, and found reactions that correctly identified the predators, the prey, and the dynamical rules governing their interactions.
"This is a tool in a suite of emerging 'automated science' tools researchers can use if they have data from some experiment, and they want the computer to help them understand what’s going on--but in the end, it's the scientist who has to give meaning to these models," says Cornell professor Hod Lipson.
From Cornell Chronicle
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