Markus Hainy,David J Price,Olivier Restif et al.
Markus Hainy et al.
Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. This issue is compounded further when ...
Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics [0.03%]
基于贝叶斯大样本渐近理论的随机游走梅特罗波利斯算法的最优缩放比例研究
Sebastian M Schmon,Philippe Gagnon
Sebastian M Schmon
High-dimensional limit theorems have been shown useful to derive tuning rules for finding the optimal scaling in random walk Metropolis algorithms. The assumptions under which weak convergence results are proved are, however, restrictive: t...
Stochastic approximation cut algorithm for inference in modularized Bayesian models [0.03%]
模块化贝叶斯模型推理的随机近似切割算法
Yang Liu,Robert J B Goudie
Yang Liu
Bayesian modelling enables us to accommodate complex forms of data and make a comprehensive inference, but the effect of partial misspecification of the model is a concern. One approach in this setting is to modularize the model and prevent...
Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes [0.03%]
广义马尔可夫调制泊松过程的精确且计算有效的贝叶斯推断方法研究
Flávio B Gonçalves,Lívia M Dutra,Roger W C Silva
Flávio B Gonçalves
Statistical modeling of temporal point patterns is an important problem in several areas. The Cox process, a Poisson process where the intensity function is stochastic, is a common model for such data. We present a new class of unidimension...
Luis A García-Escudero,Agustín Mayo-Iscar,Marco Riani
Luis A García-Escudero
A new methodology for constrained parsimonious model-based clustering is introduced, where some tuning parameter allows to control the strength of these constraints. The methodology includes the 14 parsimonious models that are often applied...
Bayesian inference for continuous-time hidden Markov models with an unknown number of states [0.03%]
具有未知状态数的连续时间隐藏马尔可夫模型的贝叶斯推断
Yu Luo,David A Stephens
Yu Luo
We consider the modeling of data generated by a latent continuous-time Markov jump process with a state space of finite but unknown dimensions. Typically in such models, the number of states has to be pre-specified, and Bayesian inference f...
Thomas Maullin-Sapey,Thomas E Nichols
Thomas Maullin-Sapey
The analysis of longitudinal, heterogeneous or unbalanced clustered data is of primary importance to a wide range of applications. The linear mixed model (LMM) is a popular and flexible extension of the linear model specifically designed fo...
Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state-space models [0.03%]
状态空间模型中有效推理的反向粒子采样时空阻隔方法
Jacob Vorstrup Goldman,Sumeetpal S Singh
Jacob Vorstrup Goldman
We propose a novel blocked version of the continuous-time bouncy particle sampler of Bouchard-Côté et al. (J Am Stat Assoc 113(522):855-867, 2018) which is applicable to any differentiable probability density. This alternative implementat...
Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data [0.03%]
大型美国枪击事件数据的自激励随机过程的可扩展贝叶斯推理方法研究
Andrew J Holbrook,Charles E Loeffler,Seth R Flaxman et al.
Andrew J Holbrook et al.
The Hawkes process and its extensions effectively model self-excitatory phenomena including earthquakes, viral pandemics, financial transactions, neural spike trains and the spread of memes through social networks. The usefulness of these s...
Santeri Karppinen,Matti Vihola
Santeri Karppinen
Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in st...