Name
Abstract | ||
|---|---|---|
This paper modifies Jaynes's axioms of plausible reasoning and derives the minimum relative entropy principle, Bayes's rule, as well as maximum likelihood from first principles. The new axioms, which I call the Optimum Information Principle, is applicable whenever the decision maker is given the data and the relevant background information. These axioms provide an answer to the question "why maximize entropy when faced with incomplete information?" |
Year | Venue | Keywords |
|---|---|---|
2011 | Clinical Orthopaedics and Related Research | bayesian inference,information theory,decision maker,maximum likelihood,incomplete information,maximum entropy,first principle,data analysis |
Field | DocType | Volume |
Econometrics,Mathematical optimization,Transfer entropy,Mathematical economics,Bayesian inference,Maximum entropy thermodynamics,Information diagram,Joint entropy,Principle of maximum entropy,Conditional entropy,Kullback–Leibler divergence,Mathematics | Journal | abs/1103.2 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexis Akira Toda | 1 | 0 | 1.01 |