In this blog, I will introduce the Generalised Maximum Entropy principle as presented in (Kesavan and Kapur 1989). To generalise MaxEnt, the paper explicitly expresses its three probabilistic entities, namely, the entropy measure, the set of moment constraints and the probability distribution, and then examines its consequences, e.g., the inverse MaxEnt. The paper links MaxEntContinue reading “The Generalised Maximum Entropy Principle”

# Author Archives: gzheshan

## Deriving PLA from MaxCal

The principle of least action (PLA), or more accurately, the principle of stationary action, is one first principle in physics. PLA offers the deepest explanatory power of our external reality. In this blog, I shall summarise a beautiful paper “Principle of maximum caliber and quantum physics” [1]. I shall omit the part of quantum physicsContinue reading “Deriving PLA from MaxCal”

## Inference of Gene Regulatory Networks using MaxEnt

In my first blog, I introduced the principle of maximum entropy (MaxEnt). Here I shall use an example of inferring Gene Regulatory Networks as proposed [1] to see how to apply MaxEnt. Recalled that with MaxEnt, any problem of interest is a problem of inferring the least biased or the most uncertain probability distributions fromContinue reading “Inference of Gene Regulatory Networks using MaxEnt”

## Principle of Maximum Entropy

This is the first blog of a series to argue a logical but controversial view: biomedicine as inference. In this first one, I am going to introduce the principle of Maximum Entropy (MaxEnt). The principle was proposed by Edward Jaynes in 1957. MaxEnt and its more general form Maximum Calibre (MaxCal) have been applied inContinue reading “Principle of Maximum Entropy”