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 ...

Free Photo Think back to ancient leaders who looked to the stars or the flight patterns of birds just to predict the future.

To determine maximum aggregate component materiality levels, we first use the cumulative binomial distribution to derive the maximum number of components that can be allowed to simultaneously contain ...

Traders in bonds and credit default swaps are bombarded with information on the default probabilities implied by credit spreads using a simple ratio. This ratio predicts that the credit spread will be ...