Prof. Jaynes would doubtless be surprised by the power of algorithms such as Markov Chain Monte Carlo, importance sampling, and particle filtering. The latter method is turning out to be one of the most fundamental and powerful tools in AI and robotics. A particle filter-like process has also been proposed to lie at the root of cognition, see Lee and Mumford “Hierarchical Bayesian Inference in the Visual Cortex”.
The central difficulty with Bayesian reasoning is its deep, deep intractability. Some probability distributions just can’t be modeled, other than by random sampling.
Prof. Jaynes would doubtless be surprised by the power of algorithms such as Markov Chain Monte Carlo, importance sampling, and particle filtering. The latter method is turning out to be one of the most fundamental and powerful tools in AI and robotics. A particle filter-like process has also been proposed to lie at the root of cognition, see Lee and Mumford “Hierarchical Bayesian Inference in the Visual Cortex”.
The central difficulty with Bayesian reasoning is its deep, deep intractability. Some probability distributions just can’t be modeled, other than by random sampling.