@article{MalemShinitskiOjedaOpper2022,
  author    = {Malem-Shinitski, Noa and Ojeda, Cesar and Opper, Manfred},
  title     = {Variational bayesian inference for nonlinear hawkes process with gaussian process self-effects},
  series = {Entropy},
  volume    = {24},
  journal   = {Entropy},
  number    = {3},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1099-4300},
  doi       = {10.3390/e24030356},
  pages     = {22},
  year      = {2022},
  abstract  = {Traditionally, Hawkes processes are used to model time-continuous point processes with history dependence. Here, we propose an extended model where the self-effects are of both excitatory and inhibitory types and follow a Gaussian Process. Whereas previous work either relies on a less flexible parameterization of the model, or requires a large amount of data, our formulation allows for both a flexible model and learning when data are scarce. We continue the line of work of Bayesian inference for Hawkes processes, and derive an inference algorithm by performing inference on an aggregated sum of Gaussian Processes. Approximate Bayesian inference is achieved via data augmentation, and we describe a mean-field variational inference approach to learn the model parameters. To demonstrate the flexibility of the model we apply our methodology on data from different domains and compare it to previously reported results.},
  language  = {en}
}
@phdthesis{MalemShinitski2023,
  author    = {Malem-Shinitski, Noa},
  title     = {Bayesian inference and modeling for point processes with applications from neuronal activity to scene viewing},
  doi       = {10.25932/publishup-61495},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-614952},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 129},
  year      = {2023},
  abstract  = {Point processes are a common methodology to model sets of events. From earthquakes to social media posts, from the arrival times of neuronal spikes to the timing of crimes, from stock prices to disease spreading -- these phenomena can be reduced to the occurrences of events concentrated in points. Often, these events happen one after the other defining a time--series. Models of point processes can be used to deepen our understanding of such events and for classification and prediction. Such models include an underlying random process that generates the events. This work uses Bayesian methodology to infer the underlying generative process from observed data. Our contribution is twofold -- we develop new models and new inference methods for these processes. We propose a model that extends the family of point processes where the occurrence of an event depends on the previous events. This family is known as Hawkes processes. Whereas in most existing models of such processes, past events are assumed to have only an excitatory effect on future events, we focus on the newly developed nonlinear Hawkes process, where past events could have excitatory and inhibitory effects. After defining the model, we present its inference method and apply it to data from different fields, among others, to neuronal activity. The second model described in the thesis concerns a specific instance of point processes --- the decision process underlying human gaze control. This process results in a series of fixated locations in an image. We developed a new model to describe this process, motivated by the known Exploration--Exploitation dilemma. Alongside the model, we present a Bayesian inference algorithm to infer the model parameters. Remaining in the realm of human scene viewing, we identify the lack of best practices for Bayesian inference in this field. We survey four popular algorithms and compare their performances for parameter inference in two scan path models. The novel models and inference algorithms presented in this dissertation enrich the understanding of point process data and allow us to uncover meaningful insights.},
  language  = {en}
}
@article{RosenblumFruehwirthMoseretal.2019,
  author    = {Rosenblum, Michael and Fr{\"u}hwirth, Martha and Moser, Maximilian and Pikovskij, Arkadij},
  title     = {Dynamical disentanglement in an analysis of oscillatory systems: an application to respiratory sinus arrhythmia},
  series = {Philosophical Transactions of the Royal Society of London, Series A : Mathematical, Physical and Engineering Sciences},
  volume    = {377},
  journal   = {Philosophical Transactions of the Royal Society of London, Series A : Mathematical, Physical and Engineering Sciences},
  number    = {2160},
  publisher = {Royal Society},
  address   = {London},
  issn      = {1364-503X},
  doi       = {10.1098/rsta.2019.0045},
  pages     = {14},
  year      = {2019},
  abstract  = {We develop a technique for the multivariate data analysis of perturbed self-sustained oscillators. The approach is based on the reconstruction of the phase dynamics model from observations and on a subsequent exploration of this model. For the system, driven by several inputs, we suggest a dynamical disentanglement procedure, allowing us to reconstruct the variability of the system's output that is due to a particular observed input, or, alternatively, to reconstruct the variability which is caused by all the inputs except for the observed one. We focus on the application of the method to the vagal component of the heart rate variability caused by a respiratory influence. We develop an algorithm that extracts purely respiratory-related variability, using a respiratory trace and times of R-peaks in the electrocardiogram. The algorithm can be applied to other systems where the observed bivariate data can be represented as a point process and a slow continuous signal, e.g. for the analysis of neuronal spiking. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.},
  language  = {en}
}
@article{BarthelmeTrukenbrodEngbertetal.2013,
  author    = {Barthelme, Simon and Trukenbrod, Hans Arne and Engbert, Ralf and Wichmann, Felix A.},
  title     = {Modeling fixation locations using spatial point processes},
  series = {Journal of vision},
  volume    = {13},
  journal   = {Journal of vision},
  number    = {12},
  publisher = {Association for Research in Vision and Opthalmology},
  address   = {Rockville},
  issn      = {1534-7362},
  doi       = {10.1167/13.12.1},
  pages     = {34},
  year      = {2013},
  abstract  = {Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.},
  language  = {en}
}