Bayesian Estimation Homework Help
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The Bayesian estimation is all together a whole different means of estimation. The unique feature of its application is that the parameters are considered to be random variables rather than assuming them to be constants whose values are not known. For a detailed account on the subject matter, refer Bayesian Estimation Homework Help. Bayes estimation performs the work of making the posterior expectation of a function maximum. At the same time it also minimizes the posterior value of loss. The study of Bayesian estimation can be lucid. For further information, go through Bayesian Estimation Homework Help.
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Aspects of Bayesian estimation
1. Prior and posterior estimates: The Bayesian estimation can make best record of small data and utilize the same. On the arrival of further new data, the estimate can be improvised too. It is better than maximum likelihood as it doesn’t ignore the small data. In experiments with lesser tries, it has a room for prior and posterior densities. For further knowledge on the subject matter, go through Bayesian Estimation Homework Help.
2. Cost function: one of the characteristic features of Bayesian estimation is the minimization problem. In such problems, the average cost or expense is sought to be minimized by searching for a particular value. For the cost solutions, many functions are used. But the most widely used ones are quadratic cost function, absolute cost function, hit or miss cost function. For more insight into the topic, refer Bayesian Estimation Homework Help.
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