arXiv subjects

Laura D'Angelo

Publications and source records attributed to Laura D'Angelo.

7 recordsLinked to original sources

arXiv2025

Decoding Neuronal Ensembles from Spatially-Referenced Calcium Traces: A Bayesian Semiparametric Approach

Understanding how neurons coordinate their activity is a fundamental question in neuroscience, with implications for learning, memory, and neurological disorders. Calcium imaging has emerged as a powerful method to observe large-scale neuronal activity in freely moving animals, providing time-resolved recordings of hundreds of neurons. However, fluorescence signals are noisy and only indirectly reflect underlying spikes of neuronal activity, complicating the extraction of reliable patterns of neuronal coordination. We introduce a fully Bayesian, semiparametric model that jointly infers spiking activity and identifies functionally coherent neuronal ensembles from calcium traces. Our approach models each neuron's spiking probability through a latent Gaussian process and encourages anatomically coherent clustering using a location-dependent stick-breaking prior. A spike-and-slab Dirichlet process captures heterogeneity in spike amplitudes while filtering out negligible events. We consider calcium imaging data from the hippocampal CA1 region of a mouse as it navigates a circular arena, a setting critical for understanding spatial memory and neuronal representation of environments. Our model uncovers spatially structured co-activation patterns among neurons and can be employed to reveal how ensemble structures vary with the animal's position.

stat.ME↗

arXiv2025

sanba: An R Package for Bayesian Clustering of Distributions via Shared Atoms Nested Models

Nested data structures arise when observations are grouped into distinct units, such as patients within hospitals or students within schools. Accounting for this hierarchical organization is essential for valid inference, as ignoring it can lead to biased estimates and poor generalization. This article addresses the challenge of clustering both individual observations and their corresponding groups while flexibly estimating group-specific densities. Bayesian nested mixture models offer a principled and robust framework for this task. However, their practical use has often been limited by computational complexity. To overcome this barrier, we present sanba, an R package for Bayesian analysis of grouped data using nested mixture models with a shared set of atoms, a structure recently introduced in the statistical literature. The package provides multiple inference strategies, including state-of-the-art Markov Chain Monte Carlo routines and variational inference algorithms tailored for large-scale datasets. All core functions are implemented in C++ and seamlessly integrated into R, making sanba a fast and user-friendly tool for fitting nested mixture models with modern Bayesian algorithms.

stat.CO↗

arXiv2025

Dependent Dirichlet processes via thinning

When analyzing data from multiple sources, it is often convenient to strike a careful balance between two goals: capturing the heterogeneity of the samples and sharing information across them. We introduce a novel framework to model a collection of samples using dependent Dirichlet processes constructed through a thinning mechanism. The proposed approach modifies the stick-breaking representation of the Dirichlet process by thinning, that is, setting equal to zero a random subset of the beta random variables used in the original construction. This results in a collection of dependent random distributions that exhibit both shared and unique atoms, with the shared ones assigned distinct weights in each distribution. The generality of the construction allows expressing a wide variety of dependence structures among the elements of the generated random vectors. Moreover, its simplicity facilitates the characterization of several theoretical properties and the derivation of efficient computational methods for posterior inference. A simulation study illustrates how a modeling approach based on the proposed process reduces uncertainty in group-specific inferences while preventing excessive borrowing of information when the data indicate it is unnecessary. This added flexibility improves the accuracy of posterior inference, outperforming related state-of-the-art models. An application to the Collaborative Perinatal Project data highlights the model's capability to estimate group-specific densities and uncover a meaningful partition of the observations, both within and across samples, providing valuable insights into the underlying data structure.

stat.ME↗

arXiv2024

MuCol Milestone Report No. 5: Preliminary Parameters

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

physics.acc-ph↗

arXiv2024

A finite-infinite shared atoms nested model for the Bayesian analysis of large grouped data

The use of hierarchical mixture priors with shared atoms has recently flourished in the Bayesian literature for partially exchangeable data. Leveraging on nested levels of mixtures, these models allow the estimation of a two-layered data partition: across groups and across observations. This paper discusses and compares the properties of such modeling strategies when the mixing weights are assigned either a finite-dimensional Dirichlet distribution or a Dirichlet process prior. Based on these considerations, we introduce a novel hierarchical nonparametric prior based on a finite set of shared atoms, a specification that enhances the flexibility of the induced random measures and the availability of fast posterior inference. To support these findings, we analytically derive the induced prior correlation structure and partially exchangeable partition probability function. Additionally, we develop a novel mean-field variational algorithm for posterior inference to boost the applicability of our nested model to large multivariate data. We then assess and compare the performance of the different shared-atom specifications via simulation. We also show that our variational proposal is highly scalable and that the accuracy of the posterior density estimate and the estimated partition is comparable with state-of-the-art Gibbs sampler algorithms. Finally, we apply our model to a real dataset of Spotify's song features, simultaneously segmenting artists and songs with similar characteristics.

stat.ME↗

arXiv2022

Efficient posterior sampling for Bayesian Poisson regression

Poisson log-linear models are ubiquitous in many applications, and one of the most popular approaches for parametric count regression. In the Bayesian context, however, there are no sufficient specific computational tools for efficient sampling from the posterior distribution of parameters, and standard algorithms, such as random walk Metropolis-Hastings or Hamiltonian Monte Carlo algorithms, are typically used. Herein, we developed an efficient Metropolis-Hastings algorithm and importance sampler to simulate from the posterior distribution of the parameters of Poisson log-linear models under conditional Gaussian priors with superior performance with respect to the state-of-the-art alternatives. The key for both algorithms is the introduction of a proposal density based on a Gaussian approximation of the posterior distribution of parameters. Specifically, our result leverages the negative binomial approximation of the Poisson likelihood and the successful Pólya-gamma data augmentation scheme. Via simulation, we obtained that the time per independent sample of the proposed samplers is competitive with that obtained using the successful Hamiltonian Monte Carlo sampling, with the Metropolis-Hastings showing superior performance in all scenarios considered.

stat.CO↗

arXiv2022

Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data

Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intra-cellular calcium signals. An on-going challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time-series. In this manuscript, we propose a nested Bayesian finite mixture specification that allows the estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. Furthermore, the spikes' intensity values are also clustered within and between experimental conditions to determine the existence of common (recurring) response amplitudes. Simulation studies and the analysis of a data set from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities.

stat.AP↗