18,826 research outputs found
Information as Distinctions: New Foundations for Information Theory
The logical basis for information theory is the newly developed logic of
partitions that is dual to the usual Boolean logic of subsets. The key concept
is a "distinction" of a partition, an ordered pair of elements in distinct
blocks of the partition. The logical concept of entropy based on partition
logic is the normalized counting measure of the set of distinctions of a
partition on a finite set--just as the usual logical notion of probability
based on the Boolean logic of subsets is the normalized counting measure of the
subsets (events). Thus logical entropy is a measure on the set of ordered
pairs, and all the compound notions of entropy (join entropy, conditional
entropy, and mutual information) arise in the usual way from the measure (e.g.,
the inclusion-exclusion principle)--just like the corresponding notions of
probability. The usual Shannon entropy of a partition is developed by replacing
the normalized count of distinctions (dits) by the average number of binary
partitions (bits) necessary to make all the distinctions of the partition
An Introduction to Logical Entropy and its Relation to Shannon Entropy
The logical basis for information theory is the newly developed logic of partitions that is dual to the usual Boolean logic of subsets. The key concept is a "distinction" of a partition, an ordered pair of elements in distinct blocks of the partition. The logical concept of entropy based on partition logic is the normalized counting measure of the set of distinctions of a partition on a finite set--just as the usual logical notion of probability based on the Boolean logic of subsets is the normalized counting measure of the subsets (events). Thus logical entropy is a measure on the set of ordered pairs, and all the compound notions of entropy (join entropy, conditional entropy, and mutual information) arise in the usual way from the measure (e.g., the inclusion-exclusion principle)--just like the corresponding notions of probability. The usual Shannon entropy of a partition is developed by replacing the normalized count of distinctions (dits) by the average number of binary partitions (bits) necessary to make all the distinctions of the partition
On the relation between plausibility logic and the maximum-entropy principle: a numerical study
What is the relationship between plausibility logic and the principle of
maximum entropy? When does the principle give unreasonable or wrong results?
When is it appropriate to use the rule `expectation = average'? Can
plausibility logic give the same answers as the principle, and better answers
if those of the principle are unreasonable? To try to answer these questions,
this study offers a numerical collection of plausibility distributions given by
the maximum-entropy principle and by plausibility logic for a set of fifteen
simple problems: throwing dice.Comment: 24 pages of main text and references, 8 pages of tables, 7 pages of
additional reference
Bayesian Updating, Model Class Selection and Robust Stochastic Predictions of Structural Response
A fundamental issue when predicting structural response by using mathematical models is how to treat both modeling and excitation uncertainty. A general framework for this is presented which uses probability as a multi-valued
conditional logic for quantitative plausible reasoning in the presence of uncertainty due to incomplete information. The
fundamental probability models that represent the structure’s uncertain behavior are specified by the choice of a stochastic
system model class: a set of input-output probability models for the structure and a prior probability distribution over this set
that quantifies the relative plausibility of each model. A model class can be constructed from a parameterized deterministic
structural model by stochastic embedding utilizing Jaynes’ Principle of Maximum Information Entropy. Robust predictive
analyses use the entire model class with the probabilistic predictions of each model being weighted by its prior probability, or if
structural response data is available, by its posterior probability from Bayes’ Theorem for the model class. Additional robustness
to modeling uncertainty comes from combining the robust predictions of each model class in a set of competing candidates
weighted by the prior or posterior probability of the model class, the latter being computed from Bayes’ Theorem. This higherlevel application of Bayes’ Theorem automatically applies a quantitative Ockham razor that penalizes the data-fit of more
complex model classes that extract more information from the data. Robust predictive analyses involve integrals over highdimensional spaces that usually must be evaluated numerically. Published applications have used Laplace's method of
asymptotic approximation or Markov Chain Monte Carlo algorithms
A quantum-mechanical Maxwell's demon
A Maxwell's demon is a device that gets information and trades it in for
thermodynamic advantage, in apparent (but not actual) contradiction to the
second law of thermodynamics. Quantum-mechanical versions of Maxwell's demon
exhibit features that classical versions do not: in particular, a device that
gets information about a quantum system disturbs it in the process. In
addition, the information produced by quantum measurement acts as an additional
source of thermodynamic inefficiency. This paper investigates the properties of
quantum-mechanical Maxwell's demons, and proposes experimentally realizable
models of such devices.Comment: 13 pages, Te
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