Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Probabilistically quantifying uncertainty in parameters, predictions and decisions is a crucial component of broad scientific and engineering applications. This is hover difficult if the number of parameters far exceeds the sample size. Although there are currently many methods which have guarantees for problems characterized by large random matrices, there is often a gap beten theory and practice when it comes to measures of statistical significance for matrices encountered in real-world applications.  proposes a scalable framework that utilizes state-of-the-art methods to provide approximations to the marginal posterior distributions. This framework is used to approximate marginal posterior inclusion probabilities for Bayesian variable selection.
['Willem van den Boom', 'David B. Dunson', 'Galen Reeves']
I am writing a research paper on a idea Quantifying uncertainty in variable selection with arbitrary matrices
[{"from": "human", "value": "I am writing a research paper on a idea Quantifying uncertainty in variable selection with arbitrary matrices"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Probabilistically quantifying uncertainty in parameters, predictions and decisions is a crucial component of broad scientific and engineering applications. This is hover difficult if the number of parameters far exceeds the sample size. Although there are currently many methods which have guarantees for problems characterized by large random matrices, there is often a gap beten theory and practice when it comes to measures of statistical significance for matrices encountered in real-world applications.  proposes a scalable framework that utilizes state-of-the-art methods to provide approximations to the marginal posterior distributions. This framework is used to approximate marginal posterior inclusion probabilities for Bayesian variable selection."}]