Outlier detection in state-space models using mean-shift penalisation [0.03%]
基于均值偏移惩罚的状态空间模型异常值检测方法研究
Rajan Shankar,Ines Wilms,Jakob Raymaekers et al.
Rajan Shankar et al.
State-space models (SSMs) provide a flexible framework for modelling time series data, but their reliance on Gaussian error assumptions makes them highly sensitive to outliers. We propose a robust estimation method, ROAMS, that mitigates th...
Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges [0.03%]
基于指数随机图模型的神经后验估计：评估偏差和实现挑战
Yefeng Fan,Simon Richard White
Yefeng Fan
Exponential random graph models (ERGMs) are flexible probabilistic frameworks to model statistical networks through a variety of network summary statistics. Conventional Bayesian estimation for ERGMs involves iteratively exchanging with an ...
Linxi Li,Shuangge Ma,Qingzhao Zhang
Linxi Li
Differential network analysis serves as a crucial tool in capturing variations in network rewiring patterns across different biological conditions. Real-world observational data often contain subgroup structures with differing statistical p...
Non-negative matrix factorization algorithms generally improve topic model fits [0.03%]
非负矩阵分解算法通常可以改善主题模型拟合优度
Peter Carbonetto,Abhishek Sarkar,Zihao Wang et al.
Peter Carbonetto et al.
In an effort to develop topic modeling methods that can be quickly applied to large data sets, we revisit the problem of maximum-likelihood estimation in topic models. It is known, at least informally, that maximum-likelihood estimation in ...
Dana Naderi,Christian P Robert,Kaniav Kamary et al.
Dana Naderi et al.
Efficient Bayesian model selection relies on the model evidence or marginal likelihood, whose computation often requires evaluating an intractable integral. The harmonic mean estimator (HME) has long been a standard method of approximating ...
Sicheng Liu,Alexander Fengler,Michael J Frank et al.
Sicheng Liu et al.
First passage time models describe the time it takes for a random process to exit a region of interest and are widely used across various scientific fields. Fast and accurate numerical methods for computing the likelihood function in these ...
Eric A Bai,Olivier Binette,Jerome P Reiter
Eric A Bai
Probabilistic record linkage is often used to match records from two files, in particular when the variables common to both files comprise identifiers measured with occasional errors like names and demographic variables. We consider biparti...
Accelerated inference for stochastic compartmental models with over-dispersed partial observations [0.03%]
具有过度离散部分观察结果的随机隔间模型的加速推理方法
Michael Whitehouse
Michael Whitehouse
An assumed density approximate likelihood is derived for a class of partially observed stochastic compartmental models which permit observational over-dispersion. This is achieved by treating time-varying reporting probabilities as latent v...
Till Hoffmann,Jukka-Pekka Onnela
Till Hoffmann
Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize three different classes of summaries and demonstrate their importance for correctly analyzing dimensio...
A Support vector machine-based mixture cure model for mixed case interval censored data [0.03%]
基于支持向量机的混合治愈模型在混合病例区间审查数据中的应用
Suvra Pal,Wisdom Aselisewine
Suvra Pal
We propose a semi-parametric two-component model for the analysis of mixed case interval censored (MCIC) data with a cured subgroup. Such data occurs when the time to an event of interest is only known to belong to an interval obtained from...