学术报告1

报告题目：Shape Modeling with Spline Partitions

报告时间：2023.10.13上午10:00 – 10:45

报告地点：数理楼138

摘要：

Shape modelling (with methods that output shapes) is a new and important task in Bayesian nonparametrics and bioinformatics. In this work, we focus on Bayesian nonparametric methods for capturing shapes by partitioning a space using curves. In related work, the classical Mondrian process is used to partition spaces recursively with axis-aligned cuts, and is widely applied in multi-dimensional and relational data. The Mondrian process outputs hyper-rectangles. Recently, the random tessellation process was introduced as a generalization of the Mondrian process, partitioning a domain with non-axis aligned cuts in an arbitrary dimensional space, and outputting polytopes. Motivated by these processes, in this work, we propose a novel parallelized Bayesian nonparametric approach to partition a domain with curves, enabling complex data-shapes to be acquired.

报告人：葛淑菲，上海科技大学数学科学研究所助理教授，加拿大Simon Fraser University统计学博士，本科毕业于中南大学数学与统计学院。研究领域为贝叶斯方法，统计机器学习，生物信息学等，重点关注计算生物学中的重要任务，如基因相似性分析、神经影像遗传学、疾病预测。已在Journal of Statistical Computation and Simulation，Cell Discovery，NeurIPS等国际期刊和会议发表多篇论文。

学术报告2

报告题目：Accelerating approximate Bayesian computation methods based on Gaussian processes

报告时间：2023.10.13上午10:45 – 11:30

报告地点：数理楼138

摘要：

Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that

targets for problems with intractable or missing likelihood function. It approximates the posterior distribution by utilizing simulators to draw synthetic data. However, ABC is computationally intensive for complex models in which simulating synthetic data is very expensive. In this article, we propose an early rejection Markov chain Monte Carlo (ejMCMC) sampler based on Gaussian processes to accelerate inference speed. We early reject samples in the first stage of the kernel using a discrepancy model, in which the discrepancy between the simulated and observed data is modeled by Gaussian process (GP). Hence, the synthetic data is generated only if the parameter space is worth exploring. We demonstrate from theory, simulation experiments, and real data analysis that the new algorithm significantly improves inference efficiency compared to existing early-rejection MCMC algorithms. In addition, we employ our proposed method within an ABC sequential Monte Carlo (SMC) sampler. In our numerical experiments, we use examples of ordinary differential equations, stochastic differential equations, and delay differential equations to demonstrate the effectiveness of the proposed algorithm.

报告人：汪时嘉，南开大学统计与数据科学学院副教授，研究领域为统计计算、统计机器学习，重点关注蒙特卡罗方法，如MCMC方法和SMC方法。已在Journal of Computational and Graphical Statistics, Journal of Computational Biology, NeurIPS等国际期刊和会议发表多篇论文。