Introduction
Bayesian learning basically means to get a computer to utilize information to do a task in the best possible manner. Bayesian means the optimization of deduction with limited data. The capacity to use the nervous system in times of doubt in a way close to the best shown by Bayesian statistics. Work in Machine learning refers to the use of a bunch of digits from information. Often, this word Bayesian is used in relation to neuroscience and behavioral science. The studies on these try to explain the nervous systems cognitive ability in relation to statistical settings (Helmholtz, 1962 P. 23). Many times it is assumed that the brain consistently has a probabilistic model that keeps updating by the neural system process of the sensory data with the use models that are closely related to the Bayesian probability.
There are quite a number of approaches that have been developed to connect Bayesian thoughts to the functions of the brain. Psychophysics has it that most items of the human perception are formulated with this Bayesian format (Nasser, 1967 P. 14). This method is keen on the behavioral outcome as the result of the brain processing some information. Neural coding uses hierarchical temporal memory which stems from the Bayesian system of Markov chains. Electrophysiology studies are based on the illustration of probabilities in the brain. Predictive coding is a genetic credible way of inferring the cause of sensory based on reducing forecast error (Ghahramani, 2004). The brain computer interface refers to a structure that can enable the management of machines and facilitates the communication with other persons but only via the cerebral activities, muscles not being used. Environmental management systems, synthetic limbs and spelling devices are examples of devices that can be used for control. With the advent of technology and increased research, it has been shown with no doubt that use of communication using the brain computer interface is possible.
Bayesian probability was developed by many contributors including Thomas Bayes, Richard Coxx, Laplace and Edwin Jayne. Bayes rule can be said that it provides us with logic of uncertainty. To get a good grasp of how the Bayesian learning works we can look at an example by answering an question; what is B? It usually contains a dependent variable and independent variable for instance, given A what is B. Within distinct and axiomatic situation of propositional sense, it may not be too sensible to go with idealistic realities of the real world. The measurement of the dependent and the independent variable may be very inaccurate and the link between these two variables are invariably non deterministic.
With uncertainties in view, the theory of probability comes in order with a consistent and principled structure for meaningful deduction. Bayesian theory may be said to provide us with uncertainty logic. For the example, above we could say what is the probability of B when A has a particular value, that is, P (B/A) (Helmholtz, 1962 P. 23). The main work of machine learning is to give an approximation of P (B/A) in relation to some appropriately particular form based on a set of similar examples of A as well as B.
The main disadvantage of Bayesian method is that we have to do integration on variables and most of these calculations are logically inflexible. Therefore most of the research on this technique relies mostly on approximation. The sparse of Bayesian however shows that a number of analytical computations can give very accurate results (Nasser, 1967 P. 14). There are a number of models that have been developed to deal with computations for instance, the linear model. In the calculation of these models, common assumption is that data is usually expected to originate from smooth and not complex functions. .
A prior can be defined as the probability distribution in relation to a set of distributions that gives confidence on the probability that some of the distributions are the ones giving the distribution. The online learning and the prediction bounds are a measure for a number of classifiers which give the extent to which the classifiers are expected to give accurate results. The bias is a way of making a preference of one predictor over another. Example of bias may include the early termination of network training of the neural system and regularization.
There are quite a number of advantages of the Bayesian learning which may include: during calculation the Bayesian method will interpolation to pure engineering. The effort that the human put in comparison to a computer is reduced significantly (Ghahramani, 2004).. This model can easily specify prior over the world model thus this is an advantage over other model that do not use this format. On language, the Bayesian and the near Bayesian formats always have a linked language for pointing on the posteriors and priors which helps a great deal when dealing with the thinkers. The prior and the integration are used in the Bayesian format and have been useful worldwide. Some of these show that the manner of deciding on how to accept or prefer one predictor to the other is usually very useful and important.
However, with all the advantages of this model it has its downside. To specify a prior is very hard since information is theoretically infeasible. Generally speaking, a real number must be specified for the each setting of this true model functions. People who have a deep understanding of the Bayesian method may not notice this reasons being that; they are well versed with languages that allow compact specification of prior. Getting this know how takes a lot of effort. Also, they usually do not give the right prior but one that is convenient to the situation at hand. Another failure related to this Bayesian learning is computation approximation may be required assuming one is able to come up with the correct prior therefore there is computational feasibility. Thirdly, we can say it’s un-automatic, in some sense we can say, the think harder of the Bayesian program is an employment regulation. It gives an acclamation that as long as there are learning problems then there is always a need to have an engineer.
Conclusion
This method, Bayesian method, provides rational framework for working out inference when there are uncertainties and for learning from information. Its ideology is straight forward though it can be complex to compute the data and that to date; there are so many research directions that are still open. In several approaches taken in machine learning, accuracy is the most measured factor with which electroencephalogram trails. When adapting a structure for a fresh user, the most common factor put into consideration is the classifier will take very little time to grasp and without help from the professionals.
Reference
Kenji, D., Shin, I., Alexandra, P., & Rajesh P. (2007). Bayesian Brain: Probabilistic Approaches to Neural Coding. The MIT Press.
Helmholtz, H. (1962). Handbuch der physiologischen optik (Southall, J. P. C. (Ed.), English trans.). New York: Dover.
Ghahramani, Z. (2004). Unsupervised learning. In O. Bousquet, G. Raetsch, & U. von Luxburg (Eds.), Advanced lectures on machine learning. Berlin: Springer-Verlag.
Neisser, U. (1967). Cognitive Psychology. Appleton-Century-Crofts. New York. New York press.
Dayan, P. & Hinton, G. E. (1996). Varieties of Helmholtz machines. , Neural Networks. London. London press.
