A probabilistic approach to aggregate induction machine modeling

Machine Learning A Probabilistic Approach

A machine learning approach to student modeling

Rescarch Program, Psychological Scicnccs llivision, Offcc of Naval Research. Approved for public rcleasc; distribution unlimitcd. Rcprodiiction in wholc or part is pcrrnittcd for any purpose of the Unitcd States Govcrnmcnt. We would like to thank Duck Slccman and Kurt VanLehn for numcrous discussions about approachcs to the student modcling problem, as wcll as John Laird and John Aiidcrson for ...

A Genetic Programming Approach to Solomonoff's Probabilistic Induction

In the context of Solomonoff’s Inductive Inference theory, Induction operator plays a key role in modeling and correctly predicting the behavior of a given phenomenon. Unfortunately, this operator is not algorithmically computable. The present paper deals with a Genetic Programming approach to Inductive Inference, with reference to Solomonoff’s algorithmic probability theory, that consists in e...

Probabilistic Approach to Rock Physics Modeling

Rock physics modeling aims to provide a link between rock properties, such as porosity, lithology, and fluid saturation, and elastic attributes, such as velocities or impedances. These models are then used in quantitative seismic interpretation and reservoir characterization. However, most of the geophysical measurements are uncertain; therefore, rock physics equations must be combined with mat...

Probabilistic Modeling and Statistical Characteristics of Aggregate Wind Power

The stochasticity of the electrical power output by wind turbines poses special challenges to power system operation and planning. Increasing penetration levels of wind and other weather-driven renewable resources exacerbate the uncertainty and variability that must be managed. This chapter focuses on the probabilistic modeling and statistical characteristics of aggregated wind power in large e...