The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level anal...The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level analyses.Traditional Bayesian inference methods,particularly Markov chain Monte Carlo,face significant computational challenges when dealing with the high-dimensional parameter spaces and complex noise characteristics inherent in gravitational wave data.This review examines the emerging role of simulation-based inference methods in gravitational wave astronomy,with a focus on approaches that leverage machine-learning techniques such as normalizing flows and neural posterior estimation.We provide a comprehensive overview of the theoretical foundations underlying various simulation-based inference methods,including neural posterior estimation,neural ratio estimation,neural likelihood estimation,flow matching,and consistency models.We explore the applications of these methods across diverse gravitational wave data processing scenarios,from single-source parameter estimation and overlapping signal analysis to testing general relativity and conducting population studies.Although these techniques demonstrate speed improvements over traditional methods in controlled studies,their model-dependent nature and sensitivity to prior assumptions are barriers to their widespread adoption.Their accuracy,which is similar to that of conventional methods,requires further validation across broader parameter spaces and noise conditions.展开更多
In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probabilit...In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.展开更多
Medicinal herb collection has historical and cultural roots in many rural communities in developing countries.Areas where herb collection occurs may overlap with biodiversity hotspots and crucial habitat of endangered...Medicinal herb collection has historical and cultural roots in many rural communities in developing countries.Areas where herb collection occurs may overlap with biodiversity hotspots and crucial habitat of endangered and threatened species.However,impacts of such practices on wildlife are unknown and possibly underestimated,perhaps due to the elusive nature of such activities.We examined this phenomenon in Wolong Nature Reserve,China,a protected area in the South-Central China biodiversity hotspot that also supports a community of Tibetan,Qiang and Han people who use herb collection as a supplementary source of livelihood.We adopted a participatory approach in which we engaged local people in outlining spatial and temporal dynamics of medicinal herb collection practices.We found that the overall spatial extent of herb collection increased in the past two decades.We then overlaid herb collection maps with localities of giant panda(Ailuropoda melanoleuca)feces collected over two time points in the reserve.Using a Bayesian parameter estimation,we found evidence for declined giant panda occurrence in the areas most recently impacted by emerging medicinal herb collection.Our methodology demonstrates the potential power of integrating participatory approaches with quantitative methods for processes like herb collection that may be difficult to examine empirically.We discuss future directions for improving explanatory power and addressing uncertainty in this type of mixed-method,interdisciplinary research.This work has implications for future attempts to understand whether and how prevalent but subtle human activities may affect wildlife conservation.展开更多
基金supported by the National Key Research and Development Program of China(2021YFC2203004)the National Natural Science Foundation of China(NSFC)(12405076,12247187,and 12147103)+1 种基金the National Astronomical Data Center(NADC2023YDS-01)the Fundamental Research Funds for the Central Universities.
文摘The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level analyses.Traditional Bayesian inference methods,particularly Markov chain Monte Carlo,face significant computational challenges when dealing with the high-dimensional parameter spaces and complex noise characteristics inherent in gravitational wave data.This review examines the emerging role of simulation-based inference methods in gravitational wave astronomy,with a focus on approaches that leverage machine-learning techniques such as normalizing flows and neural posterior estimation.We provide a comprehensive overview of the theoretical foundations underlying various simulation-based inference methods,including neural posterior estimation,neural ratio estimation,neural likelihood estimation,flow matching,and consistency models.We explore the applications of these methods across diverse gravitational wave data processing scenarios,from single-source parameter estimation and overlapping signal analysis to testing general relativity and conducting population studies.Although these techniques demonstrate speed improvements over traditional methods in controlled studies,their model-dependent nature and sensitivity to prior assumptions are barriers to their widespread adoption.Their accuracy,which is similar to that of conventional methods,requires further validation across broader parameter spaces and noise conditions.
基金supported by National High-tech Research and Development Program of China (No.2011AA7014061)
文摘In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.
基金these grants:National Natural Science Foundation of China(4157151731572293)Key Laboratory of Southwest China Wildlife Resources Conservation(China West Normal University),Ministry of Education,China(XNYB17-2).
文摘Medicinal herb collection has historical and cultural roots in many rural communities in developing countries.Areas where herb collection occurs may overlap with biodiversity hotspots and crucial habitat of endangered and threatened species.However,impacts of such practices on wildlife are unknown and possibly underestimated,perhaps due to the elusive nature of such activities.We examined this phenomenon in Wolong Nature Reserve,China,a protected area in the South-Central China biodiversity hotspot that also supports a community of Tibetan,Qiang and Han people who use herb collection as a supplementary source of livelihood.We adopted a participatory approach in which we engaged local people in outlining spatial and temporal dynamics of medicinal herb collection practices.We found that the overall spatial extent of herb collection increased in the past two decades.We then overlaid herb collection maps with localities of giant panda(Ailuropoda melanoleuca)feces collected over two time points in the reserve.Using a Bayesian parameter estimation,we found evidence for declined giant panda occurrence in the areas most recently impacted by emerging medicinal herb collection.Our methodology demonstrates the potential power of integrating participatory approaches with quantitative methods for processes like herb collection that may be difficult to examine empirically.We discuss future directions for improving explanatory power and addressing uncertainty in this type of mixed-method,interdisciplinary research.This work has implications for future attempts to understand whether and how prevalent but subtle human activities may affect wildlife conservation.
