I am interested to apply Hierarchical Bayesian Approach in meta analysis and also Monte Carlo Simulation technique. I am working on a meta-analysis and am open to have colleagues on board who are well versed with the above techniques. Looking forward to positive responses
Hi RAVI,
In order to explain a set of measurement data x, we can consider a number of possible models, say M1 ; M2; … Mj; … , where each model is parameterized by the measurand θ (for example counting number per channel) and a set of nuisance parameters θj (for example background fluctuations).
The Bayesian approach to the data analysis proceeds by assigning a prior probability distribution to the parameters of each model and a prior probability to each model. Next, by using the product rule of the probability calculus, we can write the joint distribution of the data, parameters, and models
The attached file explain it . Have a look on it please.
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