Luo Xiao,Cai Li,William Checkley et al.
Luo Xiao et al.
Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smooth...
Hamiltonian Monte Carlo acceleration using surrogate functions with random bases [0.03%]
基于随机基的代理函数加速 Hamiltonian Monte Carlo算法
Cheng Zhang,Babak Shahbaba,Hongkai Zhao
Cheng Zhang
For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an ef...
Dao Nguyen,Edward L Ionides
Dao Nguyen
Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering met...
Unbiased Bayesian inference for population Markov jump processes via random truncations [0.03%]
基于随机截断的群体马尔可夫跳过程的无偏贝叶斯推断
Anastasis Georgoulas,Jane Hillston,Guido Sanguinetti
Anastasis Georgoulas
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Baye...
Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems [0.03%]
贝叶斯样条模型中测量误差问题未观察协变量的精确抽样方法研究
Anindya Bhadra,Raymond J Carroll
Anindya Bhadra
In truncated polynomial spline or B-spline models where the covariates are measured with error, a fully Bayesian approach to model fitting requires the covariates and model parameters to be sampled at every Markov chain Monte Carlo iteratio...
Luo Xiao,Vadim Zipunnikov,David Ruppert et al.
Luo Xiao et al.
We propose two fast covariance smoothing methods and associated software that scale up linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension J > 500; a rece...
Gertraud Malsiner-Walli,Sylvia Frühwirth-Schnatter,Bettina Grün
Gertraud Malsiner-Walli
In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well...
Sergio Bacallado,Persi Diaconis,Susan Holmes
Sergio Bacallado
Recent advances in Monte Carlo methods allow us to revisit work by de Finetti who suggested the use of approximate exchangeability in the analyses of contingency tables. This paper gives examples of computational implementations using Metro...
Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations [0.03%]
带有未知浓度参数的狄利克雷过程混合模型的大数据实现中的混合问题及样本抽取问题
David I Hastie,Silvia Liverani,Sylvia Richardson
David I Hastie
We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter [Formula: see text]. This paper introduces a Gibbs sampling algorithm that combines th...
Scalable estimation strategies based on stochastic approximations: Classical results and new insights [0.03%]
基于随机逼近的可扩展估计策略：经典结果与新见解
Edoardo M Airoldi,Panos Toulis
Edoardo M Airoldi
Estimation with large amounts of data can be facilitated by stochastic gradient methods, in which model parameters are updated sequentially using small batches of data at each step. Here, we review early work and modern results that illustr...