인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract It is common for movement ecologists to model individual‐level animal movement in discrete time using methods such as hidden Markov models (HMMs). Although often the fitting of HMMs is computationally efficient, the key assumptions required to model in discrete time become limiting when dealing with temporally irregular data or an animal that changes behaviour frequently, or when comparing separate analyses on different timescales. Continuous‐time models of animal movement, which can be formulated in a scale‐invariant way, avoid these complications but typically lack computational efficiency. Most continuous‐time methods only allow for inference in a Bayesian Markov chain Monte Carlo (MCMC) framework, sampling from a parameter space of high dimensionality, which has rendered them inaccessible to biologists, inhibiting their uptake. In this work, we seek to address this inaccessibility by rigorously approximating existing inference methods for a class of spatially homogeneous continuous‐time models. We have developed a methodology that involves limiting the number of switches in behavioural state and then integrating out the times of those switches, using a combination of analytical and numerical methods, known as the fast integrated continuous‐time HMM (FInCH) approach. Our method allows for rapid evaluation of the likelihood, permitting direct maximisation of the likelihood or the posterior density, or the use of off‐the‐shelf fixed‐dimension MCMC. We demonstrate this approach using a range of simulated and real data, showing that the FInCH approach competes with its discrete‐time counterparts in terms of efficiency while improving accuracy. By using spline‐based interpolation of terms in the likelihood, the method extends to large datasets while remaining competitive. We include examples with up to 100,000 observations.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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