Applicability of a Bayesian state-space model for evaluating the effects of localized culling on subsequent density changes: sika deer as a case study
Sign Up to like & getrecommendations! Published in 2017 at "European Journal of Wildlife Research"
DOI: 10.1007/s10344-017-1128-z
Abstract: At the landscape scale, localised culling is often conducted to achieve various deer management aims. However, few studies have assessed the effects of localised culling on deer population dynamics, owing to the spatially and temporally… read more here.
Keywords: deer; unit; state space; density ... See more keywords
Bayesian state estimation in the presence of slow-rate integrated measurement
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Systems Science"
DOI: 10.1080/00207721.2020.1808730
Abstract: This paper concentrates on Bayesian state estimation approach in the presence of slow-rate integrated measurements. In chemical process, some quality variables, in the sense of measuring, often have an important characteristic resulting from the time… read more here.
Keywords: estimation; slow rate; rate integrated; bayesian state ... See more keywords
Multiscale Bayesian state-space model for Granger causality analysis of brain signal
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2018.1455814
Abstract: ABSTRACT Modelling time-varying and frequency-specific relationships between two brain signals is becoming an essential methodological tool to answer theoretical questions in experimental neuroscience. In this article, we propose to estimate a frequency Granger causality statistic… read more here.
Keywords: granger; granger causality; model; bayesian state ... See more keywords
Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Power Systems"
DOI: 10.1109/tpwrs.2019.2919157
Abstract: The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection,… read more here.
Keywords: state estimation; bayesian state; estimation; estimation unobservable ... See more keywords