Physics Faculty

• SUNY Chancellor’s Award for Excellence in Teaching (2003-2004)
• UAlbany Excellence in Teaching and Advising Award (2003-2004)

• Entropic and Bayesian Inference; Information Geometry,
• Information Physics: Entropic Foundations of Quantum Mechanics, Statistical Mechanics and General Relativity.

In recent years my research has focused on the connection between physics and information.
One goal has been to develop a general framework of entropic inference that allows one to tackle the central issues in a unified manner. The framework allows one to address questions that concern the nature of information and how it is to be processed: Is information physical, or, from a Bayesian perspective, how is information related to the beliefs of rational agents? How does one update from prior probabilities to posterior probabilities when new information becomes available? Are Bayesian and maximum entropy methods compatible with each other?

My papers on entropic inference and on its applications to the foundations of statistical mechanics and of quantum mechanics can be found here.

The general framework of entropic and Bayesian inference and its application to statistical and quantum mechanics is the general subject of the course APhy-640 Information Physics.This material is collected into the following set of lectures,

“Entropic Inference and the Foundations of Physics” invited monograph published by the Brazilian Chapter of the International Society for Bayesian Analysis—ISBrA (Sao Paulo, Brazil 2012).

A short tutorial version can be found in

“Entropic Inference” in Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. by A. Mohammad-Djafari, et al., AIP Conf. Proc. 1305, 20 (2010) ).

The unified treatment of Bayesian and entropic methods first appeared in

“Updating Probabilities” (with Adom Giffin) in Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. by A. Mohammad-Djafari, AIP Conf. Proc. Vol. 872, 31 (2007).

The application of entropic inference to entropic priors appears in

“Maximum Entropy and Bayesian Data Analysis: Entropic Prior Distributions” (with Roland Preuss) Phys. Rev. E 70, 046127 (2004).

The derivation of quantum mechanics as an example of entropic inference is given in

“Entropic Dynamics, Time and Quantum Theory” J. Phys. A 44, 225303 (2011).

“Entropic Dynamics and the Quantum Measurement Problem” (with D. T. Johnson) Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. by P. Goyal et al., AIP Conf. Proc. 1443, 104(2012).

"Momentum and Uncertainty Relations in the Entropic Approach to Quantum Theory" (with S. Nawaz), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. by P. Goyal et al., AIP Conf. Proc. 1443, 112 (2012).

An application to the statistical mechanics of fluids is

“Using relative entropy to find optimal approximations: An application to simple fluids” (with Chih-Yuan Tseng), Physica A 387, 6759 (2008).

A somewhat premature attempt to tackle general relativity:

“The Information geometry of Space and Time” in Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. by K. Knuth, A. Abbas, R. Morris, and J. Castle, AIP Conf. Proc. Vol. 803, 355 (2006).

The derivation of quantum mechanics as an algebra of experimental setups was developed in

“Consistency and linearity in quantum theory” Phys. Lett. A 244, 13 (1998).

 “Consistency, amplitudes and probabilities in quantum theory” Phys. Rev. A 57, 1572 (1998).

“Insufficient reason and entropy in quantum theory” Found. Phys. 30, 227 (2000).