Bayesian Statistical Inference for Implicitly defined Probability Models

贝叶斯统计最近被用来为大量迄今难以解决的科学和技术问题提供解决方案。贝叶斯统计的成功主要归功于所谓的马尔可夫链蒙特卡罗计算技术的应用。我们的目标是通过提供快速、简单和高效的计算实现来改进这些算法。我们将利用这些结果，通过仔细量化和建模传染病的传播率、动植物物种的空间分布、调查病毒基因组的生物学理论以及人类生育能力的变化等主题来提供洞察力。

Bayesian statistics has recently been used to provide solutions for a large number of hitherto intractable problems in science and technology. The success of Bayesian statistics has mainly been due to the application of so-called Markov chain Monte Carlo computational techniques. We aim to improve these algorithms, by providing fast, simple and efficient computational implementations. We will use the results to give insight by carefully quantifying and modelling uncertainty for such topics as the transmission rate of infectious diseases, the spatial distribution of plant and animal species, investigating biological theory for the genome of a virus, and changes in human fertility.