Bayesian Learning Methods in Artificial Intelligence

This document discusses Bayesian learning methods in the context of artificial intelligence. It explores the functions, issues, and applications of these methods, including their use in medical diagnosis, finance, and spam email identification. The document also highlights the importance of Bayesian learning in pattern recognition and algorithm development.

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Bayesian Learning Methods:
The Bayesian learning methods are some specific set of approaches in the aspect of
probabilistic machine learning procedure. In the Bayesian learning methods, the model
parameters are treated as some random variables (Mossel, Olsman & Tamuz, 2016). In this
type of learning, estimation of parameters amounts to computing posterior distributions for
the random type of variables which are dependant on the data which are observed in this case.
The Bayesian leaning method involves typical type of generative models. In this aspect one
of the important exceptions is the Bayesian linear regression and this is a discriminative type
of model.
Functions of Bayesian Learnings:
The Bayesian learning methods are crucial for both the complex and simple type of
learning. For both of the cases Bayesian learning plays an important role. In the case of
complex type of learning all of the observed examples of training can incrementally increase
or decrease the estimated probability regarding a hypothesis is correct (Yuan et al., 2017). In
this learning process more flexible approach is gained rather than the algorithms which fully
discards a hypothesis if it is determined that the hypothesis is wrong in any of the single
example.
In the simple type of learning method, the Bayesian learning process functions
importantly. In the simple type of learning the Bayesian methods can accommodate the
hypotheses which can determine the probabilistic type of prediction and in this case new type
of instances can be easily classified by making a combination of multiple type of hypothesis
which are weighted by the probabilities.

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Various Issues of Bayesian Learning Methods:
Bayesian learning method is one of the important learning method in artificial
intelligence but there are some issues with this type of learning methods. In this aspect the
main issue is that it requires a preliminary knowledge of many probabilities. In such type of
cases when the probabilities are not known previously, it is often determined depending on
the background knowledge, assumptions and previously available data about underlying
distribution form (Bartlett & Cussens, 2017). Also, another issue with the Bayesian learning
method in this case is that large amount of computational cost is required for the
determination of Bayes optimal hypothesis for the general cases.
Applications of Bayesian Learning Methods:
Currently, the Bayesian methods of learning is used for various of important
applications. These leaning methods are mainly used in varieties of artificial intelligence
related applications across several of disciplines which is including medical diagnosis,
cognitive modelling, finance and learning casual networks. These Bayesian learning methods
is a crucial part of the computerized technique of pattern recognition. The Bayesian methods
are also utilized in the simulation-based techniques which includes the Monte Carlo (Gal,
Islam & Ghahramani, 2017). In this aspect the applications of Bayesian learning methods are
also used in the phylogenetics community as here various of application of it allows
evolutionary and demographic parameters so that they can be estimated in a simultaneous
way. Also, in the recent years the Bayesian learning method has been utilized in the
algorithm development so that spam emails can be identified effectively.

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References:
Bartlett, M., & Cussens, J. (2017). Integer linear programming for the Bayesian network
structure learning problem. Artificial Intelligence, 244, 258-271.
Gal, Y., Islam, R., & Ghahramani, Z. (2017, August). Deep bayesian active learning with
image data. In Proceedings of the 34th International Conference on Machine
Learning-Volume 70 (pp. 1183-1192). JMLR. org.
Mossel, E., Olsman, N., & Tamuz, O. (2016, September). Efficient bayesian learning in
social networks with gaussian estimators. In 2016 54th Annual Allerton Conference
on Communication, Control, and Computing (Allerton) (pp. 425-432). IEEE.
Yuan, S., Wang, S., Ma, M., Ji, Y., & Deng, L. (2017). Sparse Bayesian learning-based time-
variant deconvolution. IEEE Transactions on Geoscience and Remote
Sensing, 55(11), 6182-6194.