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The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

Saksman's research deals with several mathematical problem areas that involve probabilistic questions in various setups. These include probabilistic methods in mathematical physics, analysis and ...

Continuous probabilistic techniques involving simulation can help managers predict the likelihood of time and cost overruns in all types and sizes of oil and gas projects. By deriving time and cost ...

A two-step evaluation, using a classical deterministic method and a modern probabilistic one, verified uprating a 35-year-old German natural gas pipeline. Evaluation in the deterministic redesign ...

“What kind of investment do we make in technology to create flexibility?” Nuts and Bolts: Taking its cue from the Nobel-prize-winning Black-Scholes model for valuing options, ROV aims to put a ...