This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science.
This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science.
In analyzing my data I wanted to classify it with a naive Bayesian classifier. I wasn't sure I had the math right, so I wrote a tiny abstract classifier to test with. The code is pretty cool:
Incorporating Evidence in Bayesian Networks with the Select Operator - all 4 versions »
CJ Butz, F Fang - Advances in Artificial Intelligence: 18th Conference of the …, 2005 - books.google.com
development of self-healing systems capable of making inferences about their own behavior, such as diagnosing faults and performance degradations. uses a cost-efficient technique for adaptive diagnosis that combines probabilistic inference with online, active selection of the most-informative measurements called probes. Probes are end-to-end test transactions that collect information about the availability and performance of a distributed system. Given the probe results (symptoms), RAIL performs Bayesian inference in order to find the most likely explanation (cause), An important difference between RAIL's approach and ''passive'' data analysis is in RAIL's ability to select and execute probes online. This approach, called active probing, uses an information-theoretic criterion called information gain in order to select adaptively only a small set of the most informative probes at any given time; this approach significantly reduces the overall number of probes required
An Intuitive Explanation of Bayesian Reasoning Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction. By Eliezer Yudkowsky Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule",
Bayesian Methods for Hackers : An intro to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view.
Bayesian Methods for Hackers : An intro to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view.
Our in intention is to construct a repository that will allow us empirical research within our community by facilitating (1)better reproducibility of results, and (2) better comparisons among competing approach. Both of these are required to measure progress on problems that are commonly agreed upon, such as inference and learning
Bayesian probability is an interpretation of probability suggested by Bayesian theory, which holds that the concept of probability can be defined as the degree to which a person believes a proposition. Bayesian theory also suggests that Bayes' theorem can
This project contains Naive and Fishers bayesian classifiers, as described in Toby Segaran's book "Programming Collective Intelligence." The book has python implementations; this is a Java implementation.
Tom Griffith: This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science. The first section mentions several useful general references, and the others provide supplementary readings on specific topics. If you would like to suggest some additions to the list, contact Tom Griffiths.
Bayesian Networks are probabilistic structured representations of domains which have been applied to monitoring and manipulating cause and effects for modelled systems as disparate as the weather, disease and mobile telecommunications networks. Although useful, Bayesian Networks are notoriously difficult to build accurately and efficiently which has somewhat limited their application to real world problems. Ontologies are also a structured representation of knowledge, encoding facts and rules about a given domain. This paper outlines an approach to harness the knowledge and inference capabilities inherent in an ontology model to automate the construction of Bayesian Networks to accurately represent a domain of interest. The approach was implemented in the context of an adaptive, self-configuring network management system in the telecommunications domain. In this system, the ontology model has the dual function of knowledge repository and facilitator of automated workflows and the generated BN serves to monitor effects of management activity, forming part of a feedback look for self-configuration decisions and tasks.
A. Foong, Y. Li, J. Hernández-Lobato, und R. Turner. (2019)cite arxiv:1906.11537Comment: Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning.
S. Bozza, und A. O'Hagan. Between Data Science and Applied Data Analysis: Proceedings of the 26th Annual Conference of the Gesellschaft Fűr Klassifikation Ev, 26, Seite 165. University of Mannheim, Springer Verlag, (2003)
D. Willems, und L. Vuurpijl. Proceedings of the Ninth international conference on document analysis and recognition, Seite 869-873. Curitiba, Brazil, (2007)
F. Nielsen, und R. Nock. (2011)cite arxiv:1112.4221Comment: 9 pages, 3 figures; Journal of Physics A: Mathematical and Theoretical, December 2011. IOP.