### Abstract

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. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In this

paper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.

Original language | English |
---|---|

Number of pages | 14 |

Journal | Eurasip Journal on Bioinformatics and Systems Biology |

Volume | 2016:2 |

DOIs | |

Publication status | Published - 2016 |

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### ASJC Scopus subject areas

- Signal Processing
- Applied Mathematics

### Cite this

**Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.** / Zollanvari, Amin; Dougherty, Edward R.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations

AU - Zollanvari, Amin

AU - Dougherty, Edward R.

PY - 2016

Y1 - 2016

N2 - In 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 posteriordistribution 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, priorconstruction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In thispaper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.

AB - In 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 posteriordistribution 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, priorconstruction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In thispaper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.

U2 - 10.1186/s13637-016-0036-y

DO - 10.1186/s13637-016-0036-y

M3 - Article

VL - 2016:2

JO - Eurasip Journal on Bioinformatics and Systems Biology

JF - Eurasip Journal on Bioinformatics and Systems Biology

SN - 1687-4145

ER -