17 papers · ranked by Valyu relevance
David Lüdke, Enric Rabasseda Raventós, Marcel Kollovieh, Stephan Günnemann
'Stephan Günnemann'] Point processes model the distribution of random point sets in mathematical spaces, such as spatial and temporal domains, with applications in fields like seismology, neuroscience, and economics. Existing statistical and machine learning models for point processes are predominantly constrained by…
Bernabeu, Alba, Mateu, Jorge
Spatio-temporal Hawkes point processes are a particularly interesting class of stochastic point processes for modeling self-exciting behavior, in which the occurrence of one event increases the probability of other events occurring. These processes are able to handle complex interrelationships between stochastic and…
Xinhui Rong, Victor Solo
Point processes are finding growing applications in numerous fields, such as neuroscience, high frequency finance and social media. So classic problems of classification and clustering are of increasing interest. However, analytic study of misclassification error probability in multi-class classification has barely…
Jarosław Klamut, Tomasz Gubiec, Geert Verdoolaege, Rosario Nunzio Mantegna
'Rosario Nunzio Mantegna'] In many physical, social, and economic phenomena, we observe changes in a studied quantity only in discrete, irregularly distributed points in time. The stochastic process usually applied to describe this kind of variable is the continuous-time random walk (CTRW). Despite the popularity of…
Xu Wang, Ali Shojaie, Kateřina Hlaváčková-Schindler
Thanks to technological advances leading to near-continuous time observations, emerging multivariate point process data offer new opportunities for causal discovery. However, a key obstacle in achieving this goal is that many relevant processes may not be observed in practice. Naïve estimation approaches that ignore…
Sean Bellew, Ian Flint, Yan Wang
Poisson processes have become a prominent tool in species distribution modelling when analysing citizen science data based on presence records. This study examines four distinct statistical approaches, each of which utilises a different approximation to fit a Poisson point process. These include two Poisson regressions…
Ottmar Cronie
generating functionals of point processes Authors: ['Ottmar Cronie'] Deriving exact density functions for Gibbs point processes has been challenging due to their general intractability, stemming from the intractability of their normalising constants/partition functions. This paper offers a solution to this open problem…
Charlotte M. Jones‐Todd, Alec B. M. van Helsdingen
Modelling spatial and temporal patterns in ecology is imperative to understand the complex processes inherent in ecological phenomena. Log-Gaussian Cox processes are a popular choice among ecologists to describe the spatiotemporal distribution of point-referenced data. In addition, point pattern models where events…
Ryohei Shibue, Tomoharu Iwata, Joshua Glaser
Spike train modeling across large neural populations is a powerful tool for understanding how neurons code information in a coordinated manner. Recent studies have employed marked point processes in neural population modeling. The marked point process is a stochastic process that generates a sequence of events with…
Edward Appau Nketiah, Chenlong Li, Weihua Yang, Yingchuan Jing + 2 more
Space-time self-exciting point process models are introduced to capture the clustering features in crime datasets. It does particularly well in modeling social network datasets, crime and security datasets, financial datasets, and seismic datasets. However, there has been limited analysis of large crime datasets using…
Nicoletta D’Angelo, Giada Adelfio, Jorge Mateu, Ottmar Cronie
for spatial point pattern intensity estimation Authors: ['Nicoletta D’Angelo' 'Giada Adelfio' 'Jorge Mateu' 'Ottmar Cronie'] Second-order statistics play a crucial role in analysing point processes. Previous research has specifically explored locally weighted second-order statistics for point processes, offering…
Patrick F. Bloniasz, Shohei Oyama, Emily P. Stephen
Neural electrophysiological recordings arise from interacting rhythmic (oscillatory) and broadband (aperiodic) biological subprocesses. Both rhythmic and broadband processes contribute to the neural power spectrum, which decomposes the variance of a neural recording across frequencies. Although an extensive body of…
Andi Kresna Jaya, Nurtiti Sunusi, Erna Tri Herdiani
The point process model effectively represents the number of random events occurring over time through its intensity function. When events are of two types, a bivariate point process allows simultaneous analysis of each event’s intensity. This study develops a conditional intensity model for a non-homogeneous bivariate…
Siyi Wang, Xu Wang, Chenlong Li, Augustine Wong + 1 more
Rampant terrorism poses a serious threat to the national security of many countries worldwide, particularly due to separatism and extreme nationalism. This paper focuses on the development and application of a temporal self-exciting point process model to the terror data of three countries: the US, Turkey, and the…
Andrew S. Perley, Matthew Martinez, Tommaso Mercadante, Sabrina Liu + 1 more
The dynamics of heartbeat intervals provide important insights into cardiovascular and autonomic nervous system function. Conventional analytical approaches often use fixed-window averaging, which can obscure rapid changes and reduce temporal resolution. Point process models address this limitation by operating in…
Mark Sinzger-D’Angelo, Heinz Koeppl
Cellular processes are open systems, situated in a heterogeneous context, rather than operating in isolation. Chemical reaction networks (CRNs) whose reaction rates are modelled as external stochastic processes account for the heterogeneous environment when describing the embedded process. A marginal description of the…
Yuji Kaiya, Ryo Tamura, Koji Tsuda
Kinetic models are widely used in simulating the relationship between the input space and the outcome space of a chemical process. Ignoring the computational cost, complete profiling, i.e., performing simulation at all grid points in the input space, would be the best way to understand the model, because it provides us…