Tesi sul tema "Temporal Point Processes (TPPs)"
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Allain, Cédric. "Temporal point processes and scalable convolutional dictionary learning : a unified framework for m/eeg signal analysis in neuroscience". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG008.
Testo completoIn the field of non-invasive brain imaging, Magnetoencephalography and Electroencephalography (M/EEG) offer invaluable insights into neural activities. The recorded data consist of multivariate time series that provide information about cognitive processes and are often complemented by auxiliary details related to the experimental paradigm, such as timestamps of external stimuli or actions undertaken by the subjects. Additionally, the dataset may include recordings from multiple subjects, facilitating population- level analyses.This doctoral research presents a novel framework for M/EEG signal analysis that synergizes Convolutional Dictionary Learning (CDL) and Temporal Point Processes (TPPs). The work is segmented into two primary components: temporal modeling advancements and computational scalability. For temporal modeling, two novel point process models are introduced with efficient inference methods to capture task-specific neural activities. The proposed Fast Discretized Inference for Hawkes Processes (FaDIn) method also has implications for broader applications. Additionally, this work addresses the computational challenges of large-scale M/EEG data CDL-based analysis, by introducing a novel Stochastic Robust Windowing CDL algorithm. This algorithm allows to process efficiently artifact-ridden signals as well as large population studies. Population CDL was then used on the large open-access dataset Cam-CAN, shedding light on age-related neural activity
D'ANGELO, Nicoletta. "Local methods for complex spatio-temporal point processes". Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/574349.
Testo completoKaimi, Irene. "Spatial and spatio-Temporal point processes, modelling and estimation". Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525335.
Testo completoAltieri, Linda <1986>. "A Bayesian changepoint analysis on spatio-temporal point processes". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6740/1/altieri_linda_tesi.pdf.
Testo completoAltieri, Linda <1986>. "A Bayesian changepoint analysis on spatio-temporal point processes". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6740/.
Testo completoJones-Todd, Charlotte M. "Modelling complex dependencies inherent in spatial and spatio-temporal point pattern data". Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/12009.
Testo completoRodríguez, Cortés Francisco Javier. "Modelling, Estimation and Applications of Second-Order Spatio-Temporal Characteristics of Point Processes". Doctoral thesis, Universitat Jaume I, 2014. http://hdl.handle.net/10803/394025.
Testo completoThis thesis is mainly focused on developing properties and estimators for second-order characteristics of spatio-temporal point processes. First, we present a theoretical framework of spatial and spatio-temporal point processes. The rest of the thesis is organized as follows. In Chapter 2 we present a new family of optimal and positive kernels an alternative unbiased estimator for the product density function. Its performance is compare under several kernel through MISE. In Chapter 3 a new kernel estimator of spatio-temporal product density function are given and also are developed close expressions for the variance under the Poisson case. En el capítulo 4 nos centramos en los métodos de orientación de segundo orden que proporcionan una herramienta natural para el análisis de los datos de proceso Punto espaciales anisótropas. Finally, we provide a general description of the currently ongoing research projects which have emerged motivated by the close relationship with the second-order properties.
Comas, Rodriguez Carlos. "Modelling forest dynamics through the development of spatial and temporal marked point processes". Thesis, University of Strathclyde, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415363.
Testo completoAfzal, Muhammad. "Modelling temporal aspects of healthcare processes with Ontologies". Thesis, Jönköping University, JTH, Computer and Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-12781.
Testo completoThis thesis represents the ontological model for the Time Aspects for a Healthcare Organization. It provides information about activities which take place at different interval of time at Ryhov Hospital. These activities are series of actions which may be happen in predefined sequence and at predefined times or may be happen at any time in a General ward or in Emergency ward of a Ryhov Hospital.
For achieving above mentioned objective, our supervisor conducts a workshop at the start of thesis. In this workshop, the domain experts explain the main idea of ward activities. From this workshop; the author got a lot of knowledge about activities and time aspects. After this, the author start literature review for achieving valuable knowledge about ward activities, time aspects and also methodology steps which are essentials for ontological model. After developing ontological model for Time Aspects, our supervisor also conducts a second workshop. In this workshop, the author presents the model for evaluation purpose.
Díaz, Fernández Ester. "Modelling estimation and analysis of dynamic processes from image sequences using temporal random closed sets and point processes with application to the cell exocytosis and endocytosis". Doctoral thesis, Universitat de València, 2010. http://hdl.handle.net/10803/62137.
Testo completoEn esta tesis presentamos nuevos modelos y metodolog as para el an alisis de pro- cesos din amicos a partir de secuencias de im agenes, con solapamiento espacial y tem- poral de los objetos de an alisis, un fen omeno habitual en la naturaleza. El trabajo realizado se enmarca en la teor a de Procesos Puntuales y Conjuntos Aleatorios Ce- rrados (RACS), dentro de la Geometr a Estoc astica. Los modelos propuestos son una extensi on de la teor a de modelos booleanos en R2 incorporando una componente temporal. La motivaci on del trabajo fue su aplicaci on a un proyecto multidisciplinar donde analizamos la exocitosis y la endocitosis celular, procesos en que la c elula segrega o absorbe sustancias a trav es de la membrana citoplasm atica, respectivamente. El es- tudio se realiz o utilizando secuencias de im agenes obtenidas con microscop a TIRFM, donde se observan las prote nas como agrupaciones uorescentes superpuestas. Mo- delizamos las im agenes como realizaciones de un proceso estoc astico estacionario e isotr opico. Esta metodolog a permite analizar fen omenos reales en otros campos de la Ciencia con superposici on espacio-temporal de objetos con formas y duraciones aleatorias, como Geolog a, Qu mica, Comunicaciones, etc. Primero, introducimos el modelo booleano temporal. Presentamos un m etodo de estimaci on de la funci on de distribuci on de la duraci on basado en la covarianza espacio-temporal, y el estudio de simulaci on realizado. Segundo, estudiamos la in- terrelaci on entre dos procesos espacio-temporales mediante la K-funci on de Ripley, la covarianza espacio-temporal y la funci on de correlaci on para conjuntos aleatorios bivariados. Realizamos un estudio de simulaci on y una aplicaci on a la endocitosis celular. Tercero, modelizamos la distribuci on de ves culas exoc ticas (gr anulos) en el cito- plasma celular como un proceso puntual nito. Caracterizamos su distribuci on espa- cial respecto a la membrana mediante varios descriptores funcionales. Para segmentar las im agenes, desarrollamos una herramienta autom atica de detecci on de gr anulos. Hemos desarrollado una herramienta de software completa para la simulaci on y es- timaci on de modelos booleanos temporales (disponible en http : ==www:uv:es=tracs=).
Lima, Liliam Pereira de. "Avaliação da violência urbana utilizando dados de morbimortalidade hospitalar: uma abordagem temporal e espacial". Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/5/5160/tde-09102014-092541/.
Testo completoWe consider a data set with information on victims that were assisted at the emergency room of the Dr. Arthur Ribeiro de Saboya Municipal Hospital, S~ao Paulo, Brazil, from January 1, 2002 to January 11, 2003. We analyze the data chronologically (number of events along time) and spatially (geographical location). The statistical modelling is based on point processes and wavelet techniques to estimate both temporal and spatial intensities, that is, the expected numbers of events by unit time or unit area. The results indicate that the expected number of events is greater for men than for women along the whole observation period. The same is true for the expected number of accidents and that of aggressions, the former being consistently greater than the latter. The expected numbers of events for different age groups (0 to 14, 15 to 29, 30 to 59 and 60 or more) are significantly different. A neighborhood of Saboya Hospital was considered for spatial analysis, according to which it is possible to identify regions where occurrences are most frequent. The spatial distribution of the number of events is non homogeneous with high concentration mostly on Jabaquara and Cidade Ademar districts and along some big avenues (Bandeirantes, Jabaquara and Cupec^e avenues). Spatial non homogeneity of intensities is also observed for both aggressions to men and to women. The regions with the highest risks of aggression to women seam to be located away from the big avenues. When considering traffic and transport accidents separately by each day of the week, the analysis has shown both time and spatial non homogeneous distributions of events with highest estimated intensities during weekends and lowest ones on Monday and Tuesday
Filho, NarcÃlio de SÃ Pereira. "AnÃlise da dinÃmica espaÃo-temporal (1973 a 2014) das dunas de Jericoacoara, CearÃ, Brasil". Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=15910.
Testo completoDunas costeiras exercem um importante papel na manutenÃÃo do fluxo de sedimentos da zona costeira. O Parque Nacional de Jericoacoara, localizado no estado do CearÃ, regiÃo Nordeste do Brasil, possui uma morfologia pouco frequente, trata-se de um promontÃrio associado com um campo de dunas mÃveis denominadas barcanas, dunas individuais, de grande porte com formato de ferraduras que se deslocam em direÃÃo L â O. Elas realizam o by-pass, o transporte de sedimentos, essencial para a manutenÃÃo da linha de costa. Neste trabalho, foi priorizada a definiÃÃo da evoluÃÃo morfodinÃmica de dunas mÃveis isoladas (dunas Papai Noel, PÃr-do-Sol e Arraia), tendo como referencial teÃrico a anÃlise das paisagens e como procdimento tÃcnico principal a anÃlise espaÃo-temporal do recobrimento de imagens multitemporais dos satÃlites Landsat e Quickbird entre os anos de 1973 a 2014. AtravÃs da comparaÃÃo da distribuiÃÃo espaÃo temporal das morfologias dunares, nesse perÃodo de 41 anos, evidenciaram-se mudanÃas significativas na Ãrea, perÃmetro e deslocamento das dunas. Foi possÃvel constatar a aÃÃo dos fluxos de matÃria e energia vinculados com migraÃÃo continuada direcionada para a faixa de praia (setor de bypassing de sedimentos). A dinÃmica de migraÃÃo das dunas, quando analisadas apÃs as imagens de 2000, evidenciou possibilidades de alteraÃÃes dos aspectos morfolÃgicos influenciados pelo incremento do fluxo turÃstico, quando instituÃdo o PARNA de Jericoacoara. As mudanÃas foram mais significativas, sobretudo, entre os anos de 2001 a 2005, o que pode estar relacionado a uma maior intervenÃÃo humana (fluxo de turistas). A utilizaÃÃo das tÃcnicas de geoprocessamento para o mapeamento da evoluÃÃo morfodinÃmica do campo de dunas do Parque Nacional de Jericoacoara constituiu- se uma ferramenta essencial para a produÃÃo de informaÃÃes que certamente subsidiarÃo a continuidade do planejamento ambiental da referida, que se constitui como uma Unidade de ConservaÃÃo de ProteÃÃo Integral.
Coastal dunes play an important role in the sediment flow of the coastal zone. The unique morphology of the Jericoacoara National Park in the northeastern Brazilian state of Cearà consists of a promontory covered by a mobile dune field consisting of large, horseshoe-shaped dunes known locally as barcanas that migrate from east to west. These dunes are responsible for the by-pass, the transport of sediments essential for the maintenance of the coastline. The present study focused on the morphodynamic evolution of these isolated mobile dunes through the recovery of multitemporal Landsat and Quickbird satellite images from the years between 1975 and 2014. The comparison of the spatio-temporal distribution of the morphology of these dunes over this 41-year period revealed significant shifts in their area, perimeter, and movement. It was possible to confirm that the flow of material and energy were linked to a process of continuous migration in the direction of the beach (sediment bypassing sector). The dynamics of the dune migration in the years following 2000, when the national park was established, indicate possible impacts of the increase in tourism within the area on the morphology of the dunes. The changes were most significant between 2001 and 2005, possibly reflecting a greater influx of tourists and thus more intense anthropogenic impacts. The different geoprocessing techniques applied to the mapping of the morphodynamic evolution of the dune field of the Jericoacoara National Park proved to be an essential tool for the production of information that will guarantee the long-term environmental planning of this integral conservation unit.
Kubler, Samuel. "Statistical methods for the robust extraction of objects’ spatio-temporal relations in bioimaging – Application to the functional analysis of neuronal networks in vivo". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS455.
Testo completoThe neural code, i.e. how interconnected neurons can perform complex operations, allowing the quick adaptation of animals to their environment, remains an open question and an intensive field of research both in experimental and computational neurosciences. Advances in molecular biology and microscopy have recently made it possible to monitor the activity of individual neurons in living animals and, in the case of small animals containing only a few thousands of neurons, to measure the activity of the entire nervous system. However, the mathematical framework that would bridge the gap between single neuron activity and the emergent computational properties of neuronal ensembles is missing.In the thesis manuscript, we introduce a sequential statistical processing pipeline that efficiently and robustly extracts neuronal ensembles from calcium imagery of neuronal activity. In particular, we develop a Bayesian inference framework based on a biologically interpretable model to extract neuronal ensembles characterized by noise, asynchrony and overlapping. The provided tool demonstrates that a Gibbs sampling routine can efficiently estimate statistical parameters and hidden variables to uncover neuronal ensembles based on synchronization patterns both on synthetic data and on various experimental datasets from mice and zebrafish visual cortex to Hydra Vulgaris. The thesis equally develops a point process statistical framework to quantify how neuronal ensembles encode evoked stimuli or spontaneous behaviors in living animals. This versatile tool is also used for the inference of the functional connectivity of neuronal activity or the automatically calibration procedure of the spike inference algorithms applied to calcium recordings. For the providing algorithms to be largely spread in the neurobiologist community, results are supported by interpretable biological estimates, statistical evidence, rigorous mathematical proofs, and free-available software. Our contributive implementation, that goes from pixel intensity to estimated neuronal ensembles, equally identify from the synchronous firing patterns of neuronal ensembles, neurons with specific roles that can be used to predict, improve, or alter the behaviors of living animals. The provided framework unravels the emergence of collective properties from the recording of extremely varying individual signals that make the neural code still elusive
Kolodziej, Elizabeth Young. "Nonparametric Methods for Point Processes and Geostatistical Data". Thesis, 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8351.
Testo completoJi, Chunlin. "Advances in Bayesian Modelling and Computation: Spatio-Temporal Processes, Model Assessment and Adaptive MCMC". Diss., 2009. http://hdl.handle.net/10161/1609.
Testo completoThe modelling and analysis of complex stochastic systems with increasingly large data sets, state-spaces and parameters provides major stimulus to research in Bayesian nonparametric methods and Bayesian computation. This dissertation presents advances in both nonparametric modelling and statistical computation stimulated by challenging problems of analysis in complex spatio-temporal systems and core computational issues in model fitting and model assessment. The first part of the thesis, represented by chapters 2 to 4, concerns novel, nonparametric Bayesian mixture models for spatial point processes, with advances in modelling, computation and applications in biological contexts. Chapter 2 describes and develops models for spatial point processes in which the point outcomes are latent, where indirect observations related to the point outcomes are available, and in which the underlying spatial intensity functions are typically highly heterogenous. Spatial intensities of inhomogeneous Poisson processes are represented via flexible nonparametric Bayesian mixture models. Computational approaches are presented for this new class of spatial point process mixtures and extended to the context of unobserved point process outcomes. Two examples drawn from a central, motivating context, that of immunofluorescence histology analysis in biological studies generating high-resolution imaging data, demonstrate the modelling approach and computational methodology. Chapters 3 and 4 extend this framework to define a class of flexible Bayesian nonparametric models for inhomogeneous spatio-temporal point processes, adding dynamic models for underlying intensity patterns. Dependent Dirichlet process mixture models are introduced as core components of this new time-varying spatial model. Utilizing such nonparametric mixture models for the spatial process intensity functions allows the introduction of time variation via dynamic, state-space models for parameters characterizing the intensities. Bayesian inference and model-fitting is addressed via novel particle filtering ideas and methods. Illustrative simulation examples include studies in problems of extended target tracking and substantive data analysis in cell fluorescent microscopic imaging tracking problems.
The second part of the thesis, consisting of chapters 5 and chapter 6, concerns advances in computational methods for some core and generic Bayesian inferential problems. Chapter 5 develops a novel approach to estimation of upper and lower bounds for marginal likelihoods in Bayesian modelling using refinements of existing variational methods. Traditional variational approaches only provide lower bound estimation; this new lower/upper bound analysis is able to provide accurate and tight bounds in many problems, so facilitates more reliable computation for Bayesian model comparison while also providing a way to assess adequacy of variational densities as approximations to exact, intractable posteriors. The advances also include demonstration of the significant improvements that may be achieved in marginal likelihood estimation by marginalizing some parameters in the model. A distinct contribution to Bayesian computation is covered in Chapter 6. This concerns a generic framework for designing adaptive MCMC algorithms, emphasizing the adaptive Metropolized independence sampler and an effective adaptation strategy using a family of mixture distribution proposals. This work is coupled with development of a novel adaptive approach to computation in nonparametric modelling with large data sets; here a sequential learning approach is defined that iteratively utilizes smaller data subsets. Under the general framework of importance sampling based marginal likelihood computation, the proposed adaptive Monte Carlo method and sequential learning approach can facilitate improved accuracy in marginal likelihood computation. The approaches are exemplified in studies of both synthetic data examples, and in a real data analysis arising in astro-statistics.
Finally, chapter 7 summarizes the dissertation and discusses possible extensions of the specific modelling and computational innovations, as well as potential future work.
Dissertation
Rodrigues, Ângela Afonso. "Spatio-temporal modelling of tornados with R-INLA, at the county-level in Texas and Ocklahona". Master's thesis, 2017. http://hdl.handle.net/10362/34215.
Testo completoThe United States of America is the county in the world that is more prone to tornado occurrence. This fact led many researchers, for the past years, to study and formulate theories about tornado occurrence, and which factors promote tornadogenesis. The theories around tornados are always coupled with an attempt to predict their occurrence, for better disaster alertness, and response, in case they happen. At the country level, the tornado occurrence is highly studied and understood. But the same does not happen for the state level, or county level. In this thesis, it is proposed a statistical model to characterize the occurrence of tornados in a state, given physical (terrain roughness and land-cover types)and demographic properties of its counties. This model also takes into consideration the spatial and temporal dimensions, as well as a space time interaction component. This model was applied for Oklahoma and Texas. The model with the covariates fits Texas‟ tornado occurrence, but for Oklahoma, only the spatio-temporal formulation can be applied. For Texas, the model explains the covariates as being congruent with the low-level inflow hypothesis, with tornados decreasing in zones where natural barriers for the flow can be constituted. Under the Bayesian framework, maps of spatial risk and probability of tornado occurrence for Texas and Oklahoma were computed, that can be used to make predictions in the future.