Many real-world tasks include some kind of parameter estimation, ie the determination of a
parameter encoded in a probability distribution. Often, such probability distributions arise …

Computational mechanics quantifies structure in a stochastic process via its causal states,
leading to the process's minimal, optimal predictor—the ϵ-machine ϵ-machine. We extend …

The principle goal of computational mechanics is to define pattern and structure so that the
organization of complex systems can be detected and quantified. Computational mechanics …

A stochastic process' statistical complexity stands out as a fundamental property: the
minimum information required to synchronize one process generator to another. How much …

Information engines can use structured environments as a resource to generate work by
randomizing ordered inputs and leveraging the increased Shannon entropy to transfer …

Even simply defined, finite-state generators produce stochastic processes that require
tracking an uncountable infinity of probabilistic features for optimal prediction. For processes …

The predictive information required for proper trajectory sampling of a stochastic process
can be more efficiently transmitted via a quantum channel than a classical one. This recent …

Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering
arbitrarily long pasts to retain information about arbitrarily long futures requires resources …

Delay-coordinate reconstruction is a proven modeling strategy for building effective
forecasts of nonlinear time series. The first step in this process is the estimation of good …

A system's perceived simplicity depends on whether it is represented classically or
quantally. This is not so surprising, as classical and quantum physics are descriptive …