Model counts interactions in dynamic networks using Poisson processes and clusters.
problem Counting interactions in dynamic networks with unknown cluster structure.
method Developed a model using non-homogeneous Poisson processes and block modeling. Truncated to discrete time for tractability. Used an exact integrated classification likelihood criterion for estimation.
result Estimates cluster memberships and number of clusters simultaneously.
Classifies and clusters event time data using non-homogeneous Poisson process models.
problem Classifying and clustering event time data from multiple observations.
method Modeling rate functions using spline basis expansion, estimating coefficients using maximum likelihood, and assigning observations to groups based on likelihood.
result The classification and clustering approaches perform well on both synthetic and real-world data.
Develops a trading strategy for optimal liquidation in illiquid markets.
problem Optimal liquidation in illiquid markets with different market microstructures.
method Formulated as a discrete-time Markov Decision Process with a PDMP state process, modeling price impact as a linear function of a self-exciting dynamic process.
result An optimal trading strategy depends on market microstructure characteristics, with specific actions for different order sizes.
New model clusters set-valued data without specifying cluster number.
problem Clustering set-valued data and unknown number of clusters.
method Dirichlet Process mixture of Poisson random finite sets, MCMC inference.
result Model discovers extremely unbalanced clusters.
Novel connections between Neyman-Scott processes and Bayesian nonparametric mixture models enable scalable inference.
problem Efficiently modeling and detecting clusters in spatiotemporal data.
method Adapting collapsed Gibbs sampling for Neyman-Scott processes via connections to mixture of finite mixture models.
result Demonstrated scalability and effectiveness on neural spike trains and document streams.
Study character varieties of surfaces using cluster algebras and Poisson structures.
problem Character varieties of surfaces and their Poisson structures.
method Use Bonahon-Wong's trace map and cluster algebras associated with ideal triangulations.
result Recover Goldman Poisson algebra from cluster algebra structure and show automorphisms.
We construct an infinitely exchangeable process on the set $\cate$ of subsets of the power set of the natural numbers N via a Poisson point process with mean measure Λ on the power set of N. Each $E\in\cate$ has a least monotone cover in $\catf$, the collection of monotone subsets of $\cate$, an…
The paper introduces the concept of a cluster structure to define a joint distribution of the sample size and its exchangeable random partitions. The cluster structure allows the probability distribution of the random partitions of a subset of the sample to be dependent on the sample size, a feature not presented in a …
We investigate the class of σ-stable Poisson-Kingman random probability measures (RPMs) in the context of Bayesian nonparametric mixture modeling. This is a large class of discrete RPMs which encompasses most of the the popular discrete RPMs used in Bayesian nonparametrics, such as the Dirichlet process, Pitman-Yor p…
Study models market volatility with persistent and temporary impacts.
problem Microstructure of rough volatility models driven by Poisson measures.
method Existence and uniqueness of solutions for stochastic path-dependent Volterra equations.
result Volatility process converges to fractional Heston model with spikes.
Flexible models cluster RNA sequencing data.
problem Clustering discrete data from RNA sequencing studies.
method Finite mixtures of multivariate Poisson-log normal factor analyzers with constraints.
result Models give favorable clustering performance on real and simulated data.
Model for optimal cybersecurity investment considering clustered cyberattacks.
problem Optimal investment in cybersecurity to reduce system vulnerability under clustered cyberattacks.
method Developed a continuous-time stochastic model using a Hawkes process, extended Gordon-Loeb model, solved as a Markovian stochastic optimal control problem.
result Investment policies that account for attack clustering lead to more effective and responsive strategies, improving upon static and Poisson-based approaches.
Study shows subordinated Cramér-Lundberg model increases ruin probability.
problem Analyzing the impact of subordinated time-changed claims on insurance ruin probability.
method Examined a compound Poisson process modified by a Lévy subordinator.
result Probability of ruin decreases slowly with initial capital, despite unchanged total claim amount.
Paper shows minimum observation time for network recovery.
problem Inferring latent networks from event-based observations.
method Two-stage estimator using clipped and binned event data.
result Observation time of order log(d) is sufficient and necessary.
This chapter is an attempt to present a mathematical theory of compound fractional Poisson processes. The chapter begins with the characterization of a well-known Lévy process: The compound Poisson process. The semi-Markov extension of the compound Poisson process naturally leads to the compound fractional Poisson proc…
Paper optimizes clustering for multi-layer networks and discrete mixtures.
problem Optimizing clustering in multi-layer networks and discrete mixtures.
method Two-stage method: tensor-based initialization and likelihood-based refinement.
result Achieves minimax optimal error rate for multi-layer networks and discrete mixtures.
New model explains ratings and reviews succinctly.
problem Difficult to interpret complex recommendation models.
method Bayesian co-clustering of Poisson distributions.
result Model yields easily interpretable recommendations.
Unified framework for series representations and finite approximations of CRMs.
problem Challenges in exact simulation and scalable inference with infinite-activity CRMs.
method Unified framework based on size-biased sampling of Poisson point process.
result Novel series representations for generalized gamma and stable beta processes.
This tutorial explains fitting mixture distributions to data.
problem Fitting mixture distributions to data.
method Step-by-step tutorial covering two and multiple distributions, including numerical simulations.
result Detailed explanation of fitting mixture distributions, including examples and applications.
New model distinguishes Poisson processes from self-similar ones.
problem Distinguishing Poisson point processes from self-similar processes.
method Machine learning model based on inhomogeneous, compound Poisson point process.
result The model can distinguish Poisson point processes from self-similar processes.
We show that the stick-breaking construction of the beta process due to Paisley, et al. (2010) can be obtained from the characterization of the beta process as a Poisson process. Specifically, we show that the mean measure of the underlying Poisson process is equal to that of the beta process. We use this underlying re…
Efficiently estimates Hawkes process kernels using non-parametric Bayesian methods.
problem Estimating flexible Hawkes process kernels with uncertainty quantification.
method Cluster representation of Hawkes processes, Gibbs sampling, expectation maximization.
result Linear time complexity in both theoretical and empirical settings.
The fractional Poisson process (FPP) is a counting process with independent and identically distributed inter-event times following the Mittag-Leffler distribution. This process is very useful in several fields of applied and theoretical physics including models for anomalous diffusion. Contrary to the well-known Poiss…
Bracelets and theta bases match in various cluster algebras.
problem Matching bracelet and theta bases in cluster algebras.
method Comparing skein and cluster algebras, defining quantum bracelets, and analyzing cluster scattering diagrams.
result Quantum bracelets coincide with theta functions in various cluster algebras.
A deep Neyman-Scott process uses Poisson processes for efficient inference in complex point processes.
problem Efficient inference in complex hierarchical point processes.
method Developed an efficient posterior sampling via Markov chain Monte Carlo for likelihood-based inference.
result More hidden Poisson processes improve likelihood fitting and event prediction.
A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a "multi-scoop" generalization of the beta-Bernoulli process. The BNB process is augmented into a beta-gamma-gamma-Poisson hierarchical structure, and applied as a nonparametric Bayesian prior for an infi…
This paper proposes an original approach to cluster multi-component data sets, including an estimation of the number of clusters. From the construction of a minimal spanning tree with Prim's algorithm, and the assumption that the vertices are approximately distributed according to a Poisson distribution, the number of …
New Hawkes processes model spatiotemporal events with triggering and clustering.
problem Modeling self-excitatory behavior in spatiotemporal data.
method Developed a new class of spatiotemporal Hawkes processes with efficient inference method.
result Efficiently modeled and inferred spatiotemporal events with triggering and clustering.
Modeling price clustering in financial markets using discrete distributions.
problem Price clustering phenomenon in financial markets.
method Discrete price model based on mixture of double Poisson distributions with dynamic volatility and proportions.
result Higher instantaneous volatility weakens price clustering at ultra-high frequencies.
Local minimax analysis for Poisson deconvolution of discrete signals.
problem Estimating a discrete uniform signal from Poisson convolutions.
method Local minimax risk analysis of a broad class of kernels.
result Sharp estimation rates as a function of signal clustering.
New ONMF model minimizes KL divergence for better sparse data modeling.
problem Clustering and data modeling with sparse vectors.
method Developed KL-ONMF algorithm based on alternating optimization.
result KL-ONMF outperforms Frobenius-norm ONMF for document classification and hyperspectral image unmixing.
Study shows convergence rates for BSDEs approximated by compound Poisson processes.
problem Analyzing convergence rates of BSDEs driven by Lévy processes.
method Approximating Lévy processes by compound Poisson processes and studying BSDEs.
result Optimal convergence rates derived for BSDEs in L2-norm and Wasserstein distance. New method models Poisson intensity using RKHS for high-dimensional data.
problem Tractable nonparametric modeling of inhomogeneous Poisson intensity functions.
method Reproducing Kernel Hilbert Space (RKHS) formulation for intensity functions.
result Optimization of penalized likelihood can be cast as a tractable finite-dimensional problem.
New model generates clusters with sublinear growth, useful for sparse multigraphs.
problem Cluster sizes grow linearly with sample size, limiting applicability in some cases.
method Non-exchangeable random partition models based on completely random measures and Poisson embedding.
result Model generates partitions with sublinearly growing cluster sizes, controlled by parameters.
The formation of price in a financial market is modelled as a chain of Ising spin with three fundamental figures of trading. We investigate the time behaviour of the model, and we compare the results with the real EURO/USD change rate. By using the test of local Poisson hypothesis, we show that this minimal model leads…
New methods improve estimation of nonhomogeneous Poisson processes from limited data.
problem Estimating nonhomogeneous Poisson processes from limited data.
method Formulated as a learning generalization problem, proposed adaptive and data-driven binning methods.
result Improved estimation of nonhomogeneous Poisson processes with limited data.
The paper creates correlated Poisson processes from self-decomposable laws.
problem Creating non-independent Poisson processes with specific correlations.
method Using copulas and self-decomposable laws to pair exponential renewals.
result Explicit algorithms for applications in finance and queuing theory.
Proposes a model for predicting events from event streams.
problem Predicting events like part replacement and failure in manufacturing and teleservice systems.
method Non-parametric prognostic framework using MGCP modulated Poisson processes.
result MGCP prior facilitates sharing of information and analysis of flexible event patterns.
Study shows Merton model limits to Poisson process with log-normal intensity, improving default portfolio prediction.
problem Improving prediction of default portfolios using complex models.
method Applying Merton model with log-normal intensity function to Poisson process, discussing temporal correlation effects.
result Power decay model provides better generalization for long-term default portfolio data.
Software package assesses spherical data distributions and clusters.
problem Assessing and clustering spherical data distributions.
method Innovative goodness-of-fit tests and clustering algorithms using kernel-based quadratic distances.
result Efficient and mathematically sound goodness-of-fit tests for spherical data.
A new model detects complex network communities using node attributes.
problem Lack of methods integrating node attributes for community detection in attributed networks.
method BCSBM model that integrates betweenness centrality and clustering coefficient of nodes.
result BCSBM model outperforms other methods in detecting various network structures.
Paper improves fraud detection in imbalanced financial data.
problem Detecting fraud in imbalanced financial datasets.
method Uses time-varying Poisson processes for fraud prediction.
result Method outperforms baseline in imbalanced data.
Study of bandit problem with Poisson decision times and Lévy processes.
problem Continuous-time multi-armed bandit problem with Poisson decision times.
method Gittins index policy applied to spectrally one-sided Lévy processes.
result Gittins index converges to classical Lévy bandit index.
A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to …
Study of generalized double Bruhat cells and their integrations.
problem Understanding and integrating generalized double Bruhat cells in Lie groups.
method Integrating Poisson groupoids to symplectic double groupoids, relating to fission spaces of irregular singularities.
result Explicit integrations of Poisson groupoids and Morita equivalence of double groupoids.
Efficiently infers Poisson process intensity using Gaussian process with sigmoid link.
problem Estimating intensity of inhomogeneous Poisson processes efficiently.
method Variational free-form mean field optimization and sparse Laplace's method.
result Method is one order of magnitude faster than exact inference and competitive with quadratic link function models.
Random geodesics on curved surfaces form a pattern similar to random lines.
problem Understanding the geometry of random paths on curved surfaces.
method Scaling and analysis of tessellations induced by long geodesics.
result The global statistics of tessellations approach those of a Poisson line process.
We extend the common Poisson shock framework reviewed for example in Lindskog and McNeil (2003) to a formulation avoiding repeated defaults, thus obtaining a model that can account consistently for single name default dynamics, cluster default dynamics and default counting process. This approach allows one to introduce…