Bayesian Inference

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I am reading a topic on Bayesian Inference.I read books from different authors but they are all the same. I cannot see how the terms are derived.

Could anyone briefly explain what is going on and what is it that we are trying to find using this Bayesian. Bayesian is a combination of belief from past data. So Im thinking that we are making the prediction. But Im not sure of what sort of prediction. Is ther an example to this.

I need help in understanding the 1) Bayesian formalism and
2) the mean of a gaussian distribution - how are the parameters; mu and the variance are derived and how is the posterior distri bution derived as well.

By the way could anyone suggest any recommended books for this topic. Thx
 
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Answers and Replies

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Chris Hillman
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Kapur and Kesevan.
 
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EnumaElish
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In practice Bayesian inference comes down to a weighting of prior beliefs vs. actual observations. I.e. the posterior mean is a weighted average of the prior beliefs (e.g. [itex]\mu_0[/itex]) and the data average.
 

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