Maximum-Entropy and Bayesian Methods in Inverse Problems


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Bayesian inference techniques have been a fundamental part of computerized pattern recognition techniques since the late s. There is also an ever-growing connection between Bayesian methods and simulation-based Monte Carlo techniques since complex models cannot be processed in closed form by a Bayesian analysis, while a graphical model structure may allow for efficient simulation algorithms like the Gibbs sampling and other Metropolis—Hastings algorithm schemes.

As applied to statistical classification , Bayesian inference has been used in recent years to develop algorithms for identifying e-mail spam. Spam classification is treated in more detail in the article on the naive Bayes classifier.

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Solomonoff's Inductive inference is the theory of prediction based on observations; for example, predicting the next symbol based upon a given series of symbols. The only assumption is that the environment follows some unknown but computable probability distribution. Given some p and any computable but unknown probability distribution from which x is sampled, the universal prior and Bayes' theorem can be used to predict the yet unseen parts of x in optimal fashion. Bayesian inference has been applied in different Bioinformatics applications, including differentially gene expression analysis [26] [27] , single-cell classification [28] , cancer subtyping [29] , and etc.

Bayesian inference is also used in a general cancer risk model, called CIRI Continuous Individualized Risk Index , where serial measurements are incorporated to update a Bayesian model which is primarily built from prior knowledge [30] [31]. Bayesian inference can be used by jurors to coherently accumulate the evidence for and against a defendant, and to see whether, in totality, it meets their personal threshold for ' beyond a reasonable doubt '.

The benefit of a Bayesian approach is that it gives the juror an unbiased, rational mechanism for combining evidence. It may be appropriate to explain Bayes' theorem to jurors in odds form , as betting odds are more widely understood than probabilities. Alternatively, a logarithmic approach , replacing multiplication with addition, might be easier for a jury to handle. If the existence of the crime is not in doubt, only the identity of the culprit, it has been suggested that the prior should be uniform over the qualifying population.

The use of Bayes' theorem by jurors is controversial. The jury convicted, but the case went to appeal on the basis that no means of accumulating evidence had been provided for jurors who did not wish to use Bayes' theorem.

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The Court of Appeal upheld the conviction, but it also gave the opinion that "To introduce Bayes' Theorem, or any similar method, into a criminal trial plunges the jury into inappropriate and unnecessary realms of theory and complexity, deflecting them from their proper task. Gardner-Medwin [36] argues that the criterion on which a verdict in a criminal trial should be based is not the probability of guilt, but rather the probability of the evidence, given that the defendant is innocent akin to a frequentist p-value. He argues that if the posterior probability of guilt is to be computed by Bayes' theorem, the prior probability of guilt must be known.

This will depend on the incidence of the crime, which is an unusual piece of evidence to consider in a criminal trial. Consider the following three propositions:. Gardner-Medwin argues that the jury should believe both A and not-B in order to convict. A and not-B implies the truth of C, but the reverse is not true. It is possible that B and C are both true, but in this case he argues that a jury should acquit, even though they know that they will be letting some guilty people go free. See also Lindley's paradox. Bayesian epistemology is a movement that advocates for Bayesian inference as a means of justifying the rules of inductive logic.

Karl Popper and David Miller have rejected the idea of Bayesian rationalism, i. According to this view, a rational interpretation of Bayesian inference would see it merely as a probabilistic version of falsification , rejecting the belief, commonly held by Bayesians, that high likelihood achieved by a series of Bayesian updates would prove the hypothesis beyond any reasonable doubt, or even with likelihood greater than 0. The term Bayesian refers to Thomas Bayes — , who proved a special case of what is now called Bayes' theorem. However, it was Pierre-Simon Laplace — who introduced a general version of the theorem and used it to approach problems in celestial mechanics , medical statistics, reliability , and jurisprudence.

After the s, "inverse probability" was largely supplanted by a collection of methods that came to be called frequentist statistics. In the 20th century, the ideas of Laplace were further developed in two different directions, giving rise to objective and subjective currents in Bayesian practice.

Stéphane Mallat: "Deep Generative Networks as Inverse Problems"

In the objective or "non-informative" current, the statistical analysis depends on only the model assumed, the data analyzed, [45] and the method assigning the prior, which differs from one objective Bayesian to another objective Bayesian. In the subjective or "informative" current, the specification of the prior depends on the belief that is, propositions on which the analysis is prepared to act , which can summarize information from experts, previous studies, etc.

Bayesian Inference and Maximum Entropy Methods in Science and Engineering

In the s, there was a dramatic growth in research and applications of Bayesian methods, mostly attributed to the discovery of Markov chain Monte Carlo methods, which removed many of the computational problems, and an increasing interest in nonstandard, complex applications. From Wikipedia, the free encyclopedia. Method of statistical inference. Main article: Bayes' theorem. See also: Bayesian probability. This section includes a list of references , but its sources remain unclear because it has insufficient inline citations.

Please help to improve this section by introducing more precise citations. February Learn how and when to remove this template message. Main article: Cromwell's rule. Main article: Conjugate prior. Main article: Bayesian model selection. This section is empty. You can help by adding to it. July Philosophy of Science.

Retrieved Bayesian Data Analysis , Third Edition. The Annals of Mathematical Statistics. Pitman's measure of closeness: A comparison of statistical estimators. Philadelphia: SIAM. Bayesian Methods for Function Estimation. Handbook of Statistics.


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    Maximum-Entropy and Bayesian Methods in Inverse Problems Maximum-Entropy and Bayesian Methods in Inverse Problems
    Maximum-Entropy and Bayesian Methods in Inverse Problems Maximum-Entropy and Bayesian Methods in Inverse Problems
    Maximum-Entropy and Bayesian Methods in Inverse Problems Maximum-Entropy and Bayesian Methods in Inverse Problems
    Maximum-Entropy and Bayesian Methods in Inverse Problems Maximum-Entropy and Bayesian Methods in Inverse Problems
    Maximum-Entropy and Bayesian Methods in Inverse Problems Maximum-Entropy and Bayesian Methods in Inverse Problems
    Maximum-Entropy and Bayesian Methods in Inverse Problems Maximum-Entropy and Bayesian Methods in Inverse Problems

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