A probabilistic programming language is a high-level language that makes it easy for a developer to define probability models and then “solve” these models automatically. These languages incorporate random events as primitives and their runtime environment handles inference. Now, it is a matter of programming that enables a clean separation between modeling and inference. This can vastly reduce the time and effort associated with implementing new models and understanding data. Just as high-level programming languages transformed developer productivity by abstracting away the details of the processor and memory architecture, probabilistic languages promise to free the developer from the complexities of high-performance probabilistic inference.
What does it mean to perform inference automatically? Let’s compare a probabilistic program to a classical simulation such as a climate model. A simulation is a computer program that takes some initial conditions such as historical temperatures, estimates of energy input from the sun, and so on, as an input. Then it uses the programmer’s assumptions about the interactions between these variables that are captured in equations and code to produce forecasts about the climate in the future. Simulations are characterized by the fact that they only run in one direction: forward, from causes to hypothesized effects.
A probabilistic program turns this around. Given a universe of possible interactions between different elements of the climate system and a collection of observed data, we could automatically learn which interactions are most effective in explaining the observations — even if these interactions are quite complex. How does this work? In a nutshell, the probabilistic language’s runtime environment runs the program both forward and backward. It runs forward from causes to effects (data) and backward from the data to the causes. Clever implementations will trade off between these directions to efficiently home in on the most likely explanations for the observations.
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What does it mean to perform inference automatically? Let’s compare a probabilistic program to a classical simulation such as a climate model. A simulation is a computer program that takes some initial conditions such as historical temperatures, estimates of energy input from the sun, and so on, as an input. Then it uses the programmer’s assumptions about the interactions between these variables that are captured in equations and code to produce forecasts about the climate in the future. Simulations are characterized by the fact that they only run in one direction: forward, from causes to hypothesized effects.
A probabilistic program turns this around. Given a universe of possible interactions between different elements of the climate system and a collection of observed data, we could automatically learn which interactions are most effective in explaining the observations — even if these interactions are quite complex. How does this work? In a nutshell, the probabilistic language’s runtime environment runs the program both forward and backward. It runs forward from causes to effects (data) and backward from the data to the causes. Clever implementations will trade off between these directions to efficiently home in on the most likely explanations for the observations.
- More Here
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