arXiv - STAT - Computation最新文献_第3页

Sampling parameters of ordinary differential equations with Langevin dynamics that satisfy constraints 满足约束条件的朗格文动态常微分方程参数采样

arXiv - STAT - Computation Pub Date : 2024-08-28 DOI: arxiv-2408.15505

Chris Chi, Jonathan Weare, Aaron R. Dinner

{"title":"Sampling parameters of ordinary differential equations with Langevin dynamics that satisfy constraints","authors":"Chris Chi, Jonathan Weare, Aaron R. Dinner","doi":"arxiv-2408.15505","DOIUrl":"https://doi.org/arxiv-2408.15505","url":null,"abstract":"Fitting models to data to obtain distributions of consistent parameter values\u0000is important for uncertainty quantification, model comparison, and prediction.\u0000Standard Markov Chain Monte Carlo (MCMC) approaches for fitting ordinary\u0000differential equations (ODEs) to time-series data involve proposing trial\u0000parameter sets, numerically integrating the ODEs forward in time, and accepting\u0000or rejecting the trial parameter sets. When the model dynamics depend\u0000nonlinearly on the parameters, as is generally the case, trial parameter sets\u0000are often rejected, and MCMC approaches become prohibitively computationally\u0000costly to converge. Here, we build on methods for numerical continuation and\u0000trajectory optimization to introduce an approach in which we use Langevin\u0000dynamics in the joint space of variables and parameters to sample models that\u0000satisfy constraints on the dynamics. We demonstrate the method by sampling Hopf\u0000bifurcations and limit cycles of a model of a biochemical oscillator in a\u0000Bayesian framework for parameter estimation, and we obtain more than a hundred\u0000fold speedup relative to a leading ensemble MCMC approach that requires\u0000numerically integrating the ODEs forward in time. We describe numerical\u0000experiments that provide insight into the speedup. The method is general and\u0000can be used in any framework for parameter estimation and model selection.","PeriodicalId":501215,"journal":{"name":"arXiv - STAT - Computation","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating Complex HPV Dynamics Using Emulation and History Matching 利用仿真和历史匹配研究复杂的 HPV 动态变化

arXiv - STAT - Computation Pub Date : 2024-08-28 DOI: arxiv-2408.15805

Andrew Iskauskas, Jamie A. Cohen, Danny Scarponi, Ian Vernon, Michael Goldstein, Daniel Klein, Richard G. White, Nicky McCreesh

引用次数: 0

A Model-Free Method to Quantify Memory Utilization in Neural Point Processes 量化神经点过程内存利用率的无模型方法

arXiv - STAT - Computation Pub Date : 2024-08-28 DOI: arxiv-2408.15875

Gorana Mijatovic, Sebastiano Stramaglia, Luca Faes

{"title":"A Model-Free Method to Quantify Memory Utilization in Neural Point Processes","authors":"Gorana Mijatovic, Sebastiano Stramaglia, Luca Faes","doi":"arxiv-2408.15875","DOIUrl":"https://doi.org/arxiv-2408.15875","url":null,"abstract":"Quantifying the predictive capacity of a neural system, intended as the\u0000capability to store information and actively use it for dynamic system\u0000evolution, is a key component of neural information processing. Information\u0000storage (IS), the main measure quantifying the active utilization of memory in\u0000a dynamic system, is only defined for discrete-time processes. While recent\u0000theoretical work laid the foundations for the continuous-time analysis of the\u0000predictive capacity stored in a process, methods for the effective computation\u0000of the related measures are needed to favor widespread utilization on neural\u0000data. This work introduces a method for the model-free estimation of the\u0000so-called memory utilization rate (MUR), the continuous-time counterpart of the\u0000IS, specifically designed to quantify the predictive capacity stored in neural\u0000point processes. The method employs nearest-neighbor entropy estimation applied\u0000to the inter-spike intervals measured from point-process realizations to\u0000quantify the extent of memory used by a spike train. An empirical procedure\u0000based on surrogate data is implemented to compensate the estimation bias and\u0000detect statistically significant levels of memory. The method is validated in\u0000simulated Poisson processes and in realistic models of coupled cortical\u0000dynamics and heartbeat dynamics. It is then applied to real spike trains\u0000reflecting central and autonomic nervous system activities: in spontaneously\u0000growing cortical neuron cultures, the MUR detected increasing memory\u0000utilization across maturation stages, associated to emergent bursting\u0000synchronized activity; in the study of the neuro-autonomic modulation of human\u0000heartbeats, the MUR reflected the sympathetic activation occurring with\u0000postural but not with mental stress. The proposed approach offers a\u0000computationally reliable tool to analyze spike train data in computational\u0000neuroscience and physiology.","PeriodicalId":501215,"journal":{"name":"arXiv - STAT - Computation","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NetSurvival.jl: A glimpse into relative survival analysis with Julia NetSurvival.jl：使用 Julia 进行相对生存分析的一瞥

arXiv - STAT - Computation Pub Date : 2024-08-28 DOI: arxiv-2408.15655

Rim Alhajal, Oskar Laverny

{"title":"NetSurvival.jl: A glimpse into relative survival analysis with Julia","authors":"Rim Alhajal, Oskar Laverny","doi":"arxiv-2408.15655","DOIUrl":"https://doi.org/arxiv-2408.15655","url":null,"abstract":"In many population-based medical studies, the specific cause of death is\u0000unidentified, unreliable or even unavailable. Relative survival analysis\u0000addresses this scenario, outside of standard (competing risks) survival\u0000analysis, to nevertheless estimate survival with respect to a specific cause.\u0000It separates the impact of the disease itself on mortality from other factors,\u0000such as age, sex, and general population trends. Different methods were created\u0000with the aim to construct consistent and efficient estimators for this purpose.\u0000The R package relsurv is the most commonly used today in application. With\u0000Julia continuously proving itself to be an efficient and powerful programming\u0000language, we felt the need to code a pure Julia take, thus NetSurvival.jl, of\u0000the standard routines and estimators in the field. The proposed implementation\u0000is clean, future-proof, well tested, and the package is correctly documented\u0000inside the rising JuliaSurv GitHub organization, ensuring trustability of the\u0000results. Through a comprehensive comparison in terms of performance and\u0000interface to relsurv, we highlight the benefits of the Julia developing\u0000environment.","PeriodicalId":501215,"journal":{"name":"arXiv - STAT - Computation","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An invitation to adaptive Markov chain Monte Carlo convergence theory 自适应马尔可夫链蒙特卡罗收敛理论邀请函

arXiv - STAT - Computation Pub Date : 2024-08-27 DOI: arxiv-2408.14903

Pietari Laitinen, Matti Vihola

引用次数: 0

The Traceplot Thickens: MCMC Diagnostics for Non-Euclidean Spaces 迹图变厚：非欧几里得空间的 MCMC 诊断方法

arXiv - STAT - Computation Pub Date : 2024-08-27 DOI: arxiv-2408.15392

Luke Duttweiler, Jonathan Klus, Brent Coull, Sally W. Thurston

引用次数: 0

Implementing MCMC: Multivariate estimation with confidence 实施 MCMC：有把握的多变量估计

arXiv - STAT - Computation Pub Date : 2024-08-27 DOI: arxiv-2408.15396

James M. Flegal, Rebecca P. Kurtz-Garcia

引用次数: 0

Bayesian inference for the Markov-modulated Poisson process with an outcome process 带有结果过程的马尔可夫调制泊松过程的贝叶斯推理

arXiv - STAT - Computation Pub Date : 2024-08-27 DOI: arxiv-2408.15314

Yu Luo, Chris Sherlock

{"title":"Bayesian inference for the Markov-modulated Poisson process with an outcome process","authors":"Yu Luo, Chris Sherlock","doi":"arxiv-2408.15314","DOIUrl":"https://doi.org/arxiv-2408.15314","url":null,"abstract":"In medical research, understanding changes in outcome measurements is crucial\u0000for inferring shifts in a patient's underlying health condition. While data\u0000from clinical and administrative systems hold promise for advancing this\u0000understanding, traditional methods for modelling disease progression struggle\u0000with analyzing a large volume of longitudinal data collected irregularly and do\u0000not account for the phenomenon where the poorer an individual's health, the\u0000more frequently they interact with the healthcare system. In addition, data\u0000from the claim and health care system provide no information for terminating\u0000events, such as death. To address these challenges, we start from the\u0000continuous-time hidden Markov model to understand disease progression by\u0000modelling the observed data as an outcome whose distribution depends on the\u0000state of a latent Markov chain representing the underlying health state.\u0000However, we also allow the underlying health state to influence the timings of\u0000the observations via a point process. Furthermore, we create an addition\u0000\"death\" state and model the unobserved terminating event, a transition to this\u0000state, via an additional Poisson process whose rate depends on the latent state\u0000of the Markov chain. This extension allows us to model disease severity and\u0000death not only based on the types of care received but also on the temporal and\u0000frequency aspects of different observed events. We present an exact Gibbs\u0000sampler procedure that alternates sampling the complete path of the hidden\u0000chain (the latent health state throughout the observation window) conditional\u0000on the complete paths. When the unobserved, terminating event occurs early in\u0000the observation window, there are no more observed events, and naive use of a\u0000model with only \"live\" health states would lead to biases in parameter\u0000estimates; our inclusion of a \"death\" state mitigates against this.","PeriodicalId":501215,"journal":{"name":"arXiv - STAT - Computation","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Perspective to Fish Trajectory Imputation: A Methodology for Spatiotemporal Modeling of Acoustically Tagged Fish Data 鱼类轨迹推算的新视角：声学标记鱼类数据时空建模方法论

arXiv - STAT - Computation Pub Date : 2024-08-23 DOI: arxiv-2408.13220

Mahshid Ahmadian, Edward L. Boone, Grace S. Chiu

引用次数: 0

Disclosure risk assessment with Bayesian non-parametric hierarchical modelling 利用贝叶斯非参数分层模型进行信息披露风险评估

arXiv - STAT - Computation Pub Date : 2024-08-22 DOI: arxiv-2408.12521

Marco Battiston, Lorenzo Rimella

{"title":"Disclosure risk assessment with Bayesian non-parametric hierarchical modelling","authors":"Marco Battiston, Lorenzo Rimella","doi":"arxiv-2408.12521","DOIUrl":"https://doi.org/arxiv-2408.12521","url":null,"abstract":"Micro and survey datasets often contain private information about\u0000individuals, like their health status, income or political preferences.\u0000Previous studies have shown that, even after data anonymization, a malicious\u0000intruder could still be able to identify individuals in the dataset by matching\u0000their variables to external information. Disclosure risk measures are\u0000statistical measures meant to quantify how big such a risk is for a specific\u0000dataset. One of the most common measures is the number of sample unique values\u0000that are also population-unique. cite{Man12} have shown how mixed membership\u0000models can provide very accurate estimates of this measure. A limitation of\u0000that approach is that the number of extreme profiles has to be chosen by the\u0000modeller. In this article, we propose a non-parametric version of the model,\u0000based on the Hierarchical Dirichlet Process (HDP). The proposed approach does\u0000not require any tuning parameter or model selection step and provides accurate\u0000estimates of the disclosure risk measure, even with samples as small as 1$%$\u0000of the population size. Moreover, a data augmentation scheme to address the\u0000presence of structural zeros is presented. The proposed methodology is tested\u0000on a real dataset from the New York census.","PeriodicalId":501215,"journal":{"name":"arXiv - STAT - Computation","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0