Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations

Zollanvari, AminDougherty, Edward R.2017-01-062017-01-062016Zollanvari, A., & Dougherty, E. R. (2016). Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations. Eurasip Journal on Bioinformatics and Systems Biology, 2016:2. DOI: 10.1186/s13637-016-0036-yhttp://nur.nu.edu.kz/handle/123456789/2187In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior construction is critical.enAttribution-NonCommercial-ShareAlike 3.0 United StatesSignal ProcessingApplied MathematicsResearch Subject Categories::TECHNOLOGY::Engineering physicsIncorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observationsArticle