Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Common goals in classification problems are (i) obtaining predictions and (ii) identifying subsets of highly predictive variables. Bayesian classifiers quantify the uncertainty in all steps of the prediction. Hover, common Bayesian procedures can be slow in excluding features with no predictive por (Johnson & Rossell. (2010). In certain high-dimensional setups the posterior probability assigned to the correct set of predictors converges to 0 (Johnson and Rossell 2012).  study the use of non-local priors (NLP), which overcome the above mentioned limitations.  introduce a new family of NLP and derive efficient MCMC schemes.
['David Rossell', 'Donatello Telesca', 'Valen E. Johnson']
I am writing a research paper on a idea High-Dimensional Bayesian Classifiers Using Non-Local Priors
[{"from": "human", "value": "I am writing a research paper on a idea High-Dimensional Bayesian Classifiers Using Non-Local Priors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Common goals in classification problems are (i) obtaining predictions and (ii) identifying subsets of highly predictive variables. Bayesian classifiers quantify the uncertainty in all steps of the prediction. Hover, common Bayesian procedures can be slow in excluding features with no predictive por (Johnson & Rossell. (2010). In certain high-dimensional setups the posterior probability assigned to the correct set of predictors converges to 0 (Johnson and Rossell 2012).  study the use of non-local priors (NLP), which overcome the above mentioned limitations.  introduce a new family of NLP and derive efficient MCMC schemes."}]