Article
Economics
Nianling Wang, Zhusheng Lou
Summary: The stochastic volatility (SV) model is widely used to study time-varying volatility. However, the linearity assumption for transition equation in basic SV model is restrictive. To allow for nonlinearity, we proposed a semiparametric SV model that specifies a nonparametric transition equation for log-volatility using natural cubic splines. The empirical applications to Bitcoin and convertible bond return data indicate that the transition equations of their log-volatility are highly nonlinear. Taking nonlinearity into account, the semi-parametric SV model can improve the likelihood of the basic SV model both in-sample and out-of-sample.
ECONOMIC MODELLING
(2023)
Review
Statistics & Probability
Christopher Nemeth, Paul Fearnhead
Summary: MCMC algorithms are considered the gold standard technique for Bayesian inference, but the computational cost can be prohibitive for large datasets, leading to the development of scalable Monte Carlo algorithms. One type of these algorithms is SGMCMC, which reduces per-iteration cost by utilizing data subsampling techniques.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Editorial Material
Economics
Mark Bognanni
Summary: This article introduces a method for fully Bayesian inference in the VAR-SV model and compares the different effects of using the triangular algorithm and the systemwide algorithm in the MCMC algorithm.
JOURNAL OF ECONOMETRICS
(2022)
Article
Statistics & Probability
Qi Wang, Vinayak Rao, Yee Whye Teh
Summary: This paper presents an exact Markov chain Monte Carlo sampling algorithm that does not involve time-discretization error, suitable for prior simulation, posterior simulation, and parameter inference problems related to a class of diffusions. The work reshapes an existing rejection sampling algorithm for diffusions as a latent variable model, and derives an auxiliary variable Gibbs sampling algorithm targeting the associated joint distribution. Through experiments on synthetic and real datasets, superior performance over competing methods is demonstrated.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Engineering, Mechanical
P. L. Green, L. J. Devlin, R. E. Moore, R. J. Jackson, J. Li, S. Maskell
Summary: This paper discusses the optimization of the 'L-kernel' in Sequential Monte Carlo samplers to improve performance, resulting in reduced variance of estimates and fewer resampling requirements.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Adolphus Lye, Alice Cicirello, Edoardo Patelli
Summary: This tutorial paper reviews the use of advanced Monte Carlo sampling methods in Bayesian model updating for engineering applications, introducing different methods and comparing their performance. Three case studies demonstrate the advantages and limitations of these sampling techniques in parameter identification, posterior distribution sampling, and stochastic identification of model parameters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Multidisciplinary
Ke Zhang, Kailun Su, Yunhan Yao, Qingsong Li, Suan Chen
Summary: This paper presents a dynamic evaluation model of Markov chain Monte Carlo (MCMC) roundness error measurement uncertainty based on a stochastic process. The model samples the stochastic process using the MCMC method and calculates the state transition function to reflect the autocorrelation characteristics of the parameters. A comparison between high-precision and low-precision measurements verifies the accuracy and stability of the model, showing that the MCMC method is consistent with the traditional GUM method and Monte Carlo method. The MCMC method based on the stochastic process achieves dynamic evaluation of roundness error measurement uncertainty, obtaining accurate results and improving the evaluation accuracy.
Article
Statistics & Probability
Boqian Zhang, Vinayak Rao
Summary: Markov jump processes (MJPs) are continuous-time stochastic processes commonly used in various disciplines. Inference is typically done through Markov chain Monte Carlo (MCMC), which can suffer from poor mixing when sampling unknown parameters. This work proposes a new algorithm to address this issue and demonstrates superior performance compared to existing methods in experiments.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Computer Science, Interdisciplinary Applications
DanHua ShangGuan
Summary: The Monte Carlo method is a powerful tool in many research fields, but the increasing complexity of physical models and mathematical models requires efficient algorithms to overcome the computational cost.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Review
Green & Sustainable Science & Technology
D. Hou, I. G. Hassan, L. Wang
Summary: Building Energy Model (BEM) calibration is crucial for accuracy, with recent focus on stochastic Bayesian inference calibration. However, confusion remains regarding theory, strengths, limitations, and implementations. Selecting appropriate mathematical models and tools poses a challenge.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Physics, Multidisciplinary
Anna Pajor
Summary: This paper introduces a new method for estimating the Bayes factor, with simulation examples confirming its good performance. Additionally, it is found that the validity of reducing the hybrid MSV-MGARCH model to the MGARCH specification depends on the analyzed dataset and prior assumptions about model parameters.
Article
Microbiology
Philip J. Schmidt, Ellen S. Cameron, Kirsten M. Mueller, Monica B. Emelko
Summary: Diversity analysis of amplicon sequencing data has been limited to using normalized data to obtain single values of diversity metrics, ignoring the probabilistic nature of the data. This study applies statistical analysis methods to improve diversity analysis and infer source diversity using Bayesian estimation.
FRONTIERS IN MICROBIOLOGY
(2022)
Review
Automation & Control Systems
Venkat Anantharam
Summary: This paper studies discrete time reversible Markov decision processes with finite state and action spaces, and the simplification of the policy iteration algorithm in such problems, as well as the relation between the finite time evolution of reward accumulation and the Gaussian free field associated to the controlled Markov chain.
SYSTEMS & CONTROL LETTERS
(2022)
Article
Chemistry, Physical
Shiyan Wang, Anirudh Venkatesh, Doraiswami Ramkrishna, Vivek Narsimhan
Summary: A Brownian bridge is a continuous random walk guided by an effective drift to control the endpoint of a stochastic process. While widely applicable in chemical science, the main limitation is the difficulty in determining the effective drift and solving the complex Backward Fokker-Planck equation.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Computer Science, Theory & Methods
Isa Marques, Thomas Kneib, Nadja Klein
Summary: The paper discusses the special nature of circular data and develops a spatial model for Gaussian random processes with non-stationary mean and covariance structure. The study utilizes the empirical equivalence between Gaussian random fields and Gaussian Markov random fields, develops tunable priors, and uses Markov chain Monte Carlo simulation for posterior estimation.
STATISTICS AND COMPUTING
(2022)
Article
Multidisciplinary Sciences
Gennady Gorin, Mengyu Wang, Ido Golding, Heng Xu
Article
Optics
Shawn Yoshida, William Schmid, Nam Vo, William Calabrase, Lydia Kisley
Summary: This study improves the efficiency of fcsSOFI by parallelizing the least-squares curve fitting step on a GPU, implementing anomalous and two-component Brownian diffusion models, and packaging it in a user-friendly GUI. By applying fesSOFI to simulations of protein diffusion in polyacrylamide, it super-resolves locations of slower, anomalous diffusion within smaller, confined pores. These enhancements in speed, scope, and usability will facilitate the broader adoption of super-resolution correlation analysis across diverse research domains.
Review
Spectroscopy
Shawn Yoshida, Lydia Kisley
Summary: The extracellular matrix (ECM) is a crucial biophysical environment involved in physiological processes. Super-resolution fluorescence microscopy offers the potential to investigate local, nanoscale, physicochemical variations in the ECM, providing insights into molecular-level ECM processes and guiding future biomedical research directions.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Biophysics
Gennady Gorin, Lior Pachter
Summary: In this study, the impact of splicing cascades on expression dynamics is investigated. The authors examine a class of processes and associated distributions that arise from bursty promoters coupled to directed acyclic graphs of splicing. They provide time-dependent joint distributions for various species, offering insights into how splicing can regulate expression dynamics. The findings are validated through the analysis of long-read sequencing data.
BIOPHYSICAL JOURNAL
(2022)
Article
Biochemical Research Methods
Gennady Gorin, Meichen Fang, Tara Chari, Lior Pachter
Summary: We perform a thorough analysis of RNA velocity methods and propose an improved framework.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Gennady Gorin, John J. Vastola, Meichen Fang, Lior Pachter
Summary: This study investigates how cell-to-cell differences in transcription rate affect RNA count distributions. The authors introduce quantitative models to compare and contrast two biologically plausible hypotheses about transcription rate variation. They propose a framework for analyzing these models and use Bayesian model selection to identify candidate genes in single-cell transcriptomic data.
NATURE COMMUNICATIONS
(2022)
Article
Biology
Shawn Irgen-Gioro, Shawn Yoshida, Victoria Walling, Shasha Chong
Summary: Fixing cells with paraformaldehyde (PFA) can both enhance and diminish the appearance of liquid-liquid phase separation (LLPS) in living cells. PFA fixation can cause artificial droplet-like puncta to appear in cells that do not have detectable puncta in the live condition. The protein localization in fixed cells depends on the dynamics of protein-protein interactions, the overall rate of fixation, and the difference in fixation rates of different proteins.
Review
Chemistry, Physical
Shawn R. Yoshida, Barun K. Maity, Shasha Chong
Summary: Fluorescence microscopy techniques are widely used in biology for visualizing cellular and subcellular processes. Methods for fixing cells before imaging can induce redistribution of proteins and result in artifacts. This review discusses the ability of commonly used fixation methods to preserve protein localizations and the underlying mechanisms of fixation artifacts. Alternative fixation methods are also discussed to minimize artifacts. Careful selection of a fixation method is necessary to avoid artifacts in fixed-cell fluorescence microscopy.
JOURNAL OF PHYSICAL CHEMISTRY B
(2023)
Article
Biochemistry & Molecular Biology
Lizhen Chen, Zhao Zhang, Qinyu Han, Barun K. Maity, Leticia Rodrigues, Emily Zboril, Rashmi Adhikari, Su-Hyuk Ko, Xin Li, Shawn R. Yoshida, Pengya Xue, Emilie Smith, Kexin Xu, Qianben Wang, Tim Hui-Ming Huang, Shasha Chong, Zhijie Liu
Summary: This study reveals that androgen receptor (AR) forms condensates through multivalent interactions mediated by its N-terminal intrinsically disordered region (IDR) to orchestrate enhancer assembly in response to androgen signaling. Expansion of the poly(Q) track within AR IDR results in a higher AR condensation propensity, which affects its interactions with other enhancer components and its transcriptional activity.
Article
Biochemistry & Molecular Biology
Gennady Gorin, John J. Vastola, Lior Pachter
Summary: Recent experimental developments in genome-wide RNA quantification show great potential for systems biology. However, a unified mathematical framework is needed to comprehensively study the biology of living cells, taking into account technical variations in genomics assays and the stochasticity of single-molecule biology. In this paper, we review different models for RNA transcription processes and present a framework that integrates these phenomena through the manipulation of generating functions. Finally, we provide simulated scenarios and biological data to demonstrate the implications and applications of this approach.
Article
Biophysics
Gennady Gorin, Lior Pachter
Summary: Single-cell RNA sequencing data can be modeled using Markov chains to gain genome-wide insights into transcriptional physics. However, accurate analysis of the data requires careful consideration of noise sources. A length-based model of capture bias is proposed to explain the over-representation of long pre-mRNA transcripts in sequencing data, which may lead to false-positive observations. This model provides concordant parameter trends and helps identify systematic, mechanistically interpretable technical and biological differences in paired data sets.
BIOPHYSICAL REPORTS
(2023)
Article
Physics, Fluids & Plasmas
Gennady Gorin, Lior Pachter
