The compositionality of probabilistic models means that the behaviour of ... random forests or deep learning, the Automatic Statistician builds models that are composed of interpretable components ...

Here are some key ways they are used: Uncertainty Quantification: While deep learning models are powerful, they often lack built-in mechanisms for representing uncertainty. Probabilistic models ...

One virtue of probabilistic models is that they straddle the gap between cognitive science, artificial intelligence, and machine learning. The same methodology is useful for both understanding the ...

One is that the probabilistic approach of deep learning is out. And you have acknowledged that the energy-based models you are discussing have some connection back to approaches of the 1980s ...

"This is why we are working with a probabilistic model. It can monitor or control the judgment of the deep learning model. We don't just rely on the direct pattern in the information ...

The local volatility model is widely used for pricing and hedging financial derivatives. While its main appeal is its capability of reproducing any given surface of observed option prices – it ...

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...