Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract   The minimum cross-entropy principle is an established technique for design of an unknown distribution, processing linear functional constraints on the distribution. More generally, fully probabilistic design (FPD) chooses the distribution—within the knowledge-constrained set of possible distributions—for which the Kullback-Leibler divergence to the designer’s ideal distribution is minimized. These principles treat the unknown distribution deterministically. , fully probabilistic design is applied to hierarchical Bayesian models for the first time, yielding optimal design of a (possibly nonparametric) stochastic model for the unknown distribution. This equips minimum cross-entropy and FPD distributional estimates with measures of uncertainty. It enables robust choice of the optimal model, as ll as randomization of this choice. The ability to process non-linear functional constraints in the constructed distribution significantly extends the applicability of these principles. Currently available FPD procedures for (a) merging of external knowledge, (b) approximate learning and stabilized forgetting, (c) decision strategy design, and (d) local adaptive control design, are unified for the first time via the hierarchical FPD framework of this paper.
['Anthony Quinn', 'Miroslav Kárný', 'Tatiana V. Guy']
I am writing a research paper on a idea Fully probabilistic design of hierarchical Bayesian models
[{"from": "human", "value": "I am writing a research paper on a idea Fully probabilistic design of hierarchical Bayesian models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract   The minimum cross-entropy principle is an established technique for design of an unknown distribution, processing linear functional constraints on the distribution. More generally, fully probabilistic design (FPD) chooses the distribution\u2014within the knowledge-constrained set of possible distributions\u2014for which the Kullback-Leibler divergence to the designer\u2019s ideal distribution is minimized. These principles treat the unknown distribution deterministically. , fully probabilistic design is applied to hierarchical Bayesian models for the first time, yielding optimal design of a (possibly nonparametric) stochastic model for the unknown distribution. This equips minimum cross-entropy and FPD distributional estimates with measures of uncertainty. It enables robust choice of the optimal model, as ll as randomization of this choice. The ability to process non-linear functional constraints in the constructed distribution significantly extends the applicability of these principles. Currently available FPD procedures for (a) merging of external knowledge, (b) approximate learning and stabilized forgetting, (c) decision strategy design, and (d) local adaptive control design, are unified for the first time via the hierarchical FPD framework of this paper."}]