Computational Inference, Monte Carlo, and Scientific Applications

职务名称：计算推理、蒙特卡罗方法和科学应用随着自动化、高通量实验方案和数据收集技术的出现，许多科学和技术领域的研究和发现变得越来越数据驱动和计算密集。该项目的研究动机来自分子生物学、生物技术和神经科学。这些领域实验数据的快速积累已经超出了科学家分析它们的能力，需要先进的统计方法来自动化分析过程，并利用这些研究背后的复杂数据结构和广泛的科学知识。计算推理是指统计建模和推理过程，依赖于密集的计算来从大规模数据和基于知识的模型中提取信息。该项目的董事会长期目标是推进计算推理的方法，并将其应用于解决上述科学领域中的几个重要问题。几乎所有大规模计算推理过程中的一个关键步骤是通过Monte Carlo抽样研究后验密度（或研究似然函数的相关问题）。成功的采样会立即推断出研究者感兴趣的任何参数或预测。因此，该项目的第一个具体目标是开发有效的抽样复杂，多峰分布的蒙特卡罗模拟方法。这一核心计算问题的进展不仅将促进有效的计算推理，而且还将对其他科学任务（如分子结构模拟和组合优化）感兴趣。将研究三种方法：a）进化蒙特卡罗方法，其中结构的群体进化和个体结构，包括重组结构，在群体中持续竞争生存，B）通过更好的方法处理偏斜的权重分布，进一步发展序贯重要性抽样和动态重要性抽样，c）多级计算模型。混合算法结合上述方法也将进行研究。其中一些方法将用于研究理解蛋白质构象的能量景观的巨大挑战问题。该项目的第二个具体目标是为两个进一步的科学问题开发计算推理工具：i）基于隐马尔可夫模型的DNA和蛋白质序列的多重比对和聚类，以及在人类基因组编码区分析中的使用，ii）为低层次视觉任务（如纹理识别和原始草图绘制）开发分层计算模型。如果成功的话，在这个项目中开发的方法将使计算推理的更广泛的应用，也将导致在当前的科学前沿的三个相当重要的问题的直接贡献。

Title: Computational inference, Monte Carlo methods, and scientific applicationsWith the advent of automated, high-throughput experimental protocols and data collection techniques, research and discoveries in many areas of science and technology have become increasingly data driven and computation intensive. The applications motivating the research in this project arise from molecular biology, biotechnology and neural science. The rapid accumaulation of experimental data in these areas have outstriped scientists' ability to analyze them, and advanced statistical methods are needed to automate the analysis process and to exploit the complex data structure and extensive scientific knowledge underlying such studies. Computational inference refers to statistical modeling and inference procedures that rely on intensive computation to extract information from large scale data and knowledge-based models. The board, long term goal of this project is to advance the methodologies of computational inference and apply them towards the solution of several important problems in the aforementioned scientific areas. A critical step in almost all large scale computational inference procedure is the study of the posterior density through Monte Carlo sampling (or the related problem of studying the likelihood function). Successful sampling leads immediately to the inference of any parameter or prediction of interest to the investigator. Thus the first specific goal of this project is to develop Monte Carlo simulation methods that are effective in sampling complex, multimodal distributions. Advances in this core computational problem will not only facilitate effective computational inference, but will also be of interest to other scientific tasks such as simulation of molecular structures and combinatorial optimization. Three approaches will be investigated: a) an evolutionary Monte Carlo approach where a population of structures are evolved and individual structures, including recombinant ones, are continuously competing for survival in the population, b) further development of sequential importance sampling and dynamic importance sampling through better methods to handle skewed weight distributions, c) multi-level computational models. Hybrid algorithms combining the above approaches will also be investigated. Some of these methods will be used to investigate the grand-challenge problem of understanding the energy landscape of protein conformation. The second specific goal of this project is the development of computational inference tools for two further scientifc problems: i) multiple alignment and clustering of DNA and protein sequences based on hidden Markov models, and the use of these in the analysis of human genome coding regions, ii) the development of hierarchical computational models for low-level vision task such as texture recognition and primal sketching. If successful, the methods developed in this project will enable the wider application of computational inference and will also result in direct contributions to three problems of considerable importance in the current scientific frontier.