Bayesian inference, Advanced Statistics

Assignment Help:

Bayesian inference: An approach to the inference based largely on Bayes' Theorem and comprising of the below stated principal steps:

(1) Obtain the likelihood, f x q describing the process increasing the data x in terms of unknown parameters q.

(2) Obtain the previous distribution, f q expressing what is known about the q, previous to observing the data.

(3) Apply Bayes' theorem to derive posterior distribution f q x expressing that what is known about q after observing the given data.

(4) Derive suitable inference statements from posterior distribution. These might include speci?c inferences like interval estimates, point estimates or probabilities of the hypotheses or asumptions. If interest centres on particular components of q their posterior distribution is formed by the integrating out of the other parameters.

This form of inference varies from classical form of the frequentist inference in the various respects, particularly the use of prior distribution which is not present in the classical inference. It represents the investigator's knowledge and wisdom about the parameters before seeing data.

Classical statistics only makes use of the likelihood. As a result to the Bayesian every problem is unique and is considered by the investigator's beliefs about parameters expressed in the prior distribution for the speci?c or particular investigation.






Related Discussions:- Bayesian inference

Path analysis, Path analysis  is  a device for evaluating the interrelat...

Path analysis  is  a device for evaluating the interrelationships among the variables by analyzing their correlational structure. The relationships between the variables are man

Control group, In the experimental studies, the collection of individuals t...

In the experimental studies, the collection of individuals to which the experimental process of interest is not applied. In the observational studies, most often used for a collect

Data fusion, The act of combining data from heterogeneous sources with the ...

The act of combining data from heterogeneous sources with the intent of extracting information that would not be available for any single source in isolation. An example is the com

Gaussian markov random field, It is the multivariate normal random vector w...

It is the multivariate normal random vector which satisfies certain conditional independence suppositions. This can be viewed as a model framework which contains a wide range of st

Common cause failures (ccf), Common cause failures (CCF): Simultaneous fai...

Common cause failures (CCF): Simultaneous failures of the number of components due to a same reason. A reason can be external to the components, or it can be the single failure wh

Incidental parameter problem, Incidental parameter problem is a problem wh...

Incidental parameter problem is a problem which sometimes occurs when the number of parameters increases in the tandem with the number of observations. For instance, models for pa

Markov Model, How to estimate MLE for statistical anslysis using Markov Mod...

How to estimate MLE for statistical anslysis using Markov Model?

Define least significant difference test, Least significant difference test...

Least significant difference test is an approach to comparing a set of means which controls the family wise error rate at some specific level, let's assume it to be α. The hypothe

Exponential family, A family of the probability distributions of the form g...

A family of the probability distributions of the form given as   here θ is the parameter and a, b, c, d are the known functions. It includes the gamma distribution, normal dis

Write Your Message!

Captcha
Free Assignment Quote

Assured A++ Grade

Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!

All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd