Stochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be estimated from observed data. Ideally we would have measurements on all interacting chemical species in the process, observed continuously in time. However, in practice, measurements are taken only at a relatively few time-points. In some situations, only very limited observation of the process is available, such as when experimenters can only observe noisy observations on the proportion of cells that are alive. This makes the inference task even more problematic. We consider a range of data-poor scenarios and investigate the performance of various computationally intensive Bayesian algorithms in determining the posterior distribution using data on proportions from a simple birth-death process.

中文翻译：

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使用比例数据对部分观察到的生死过程进行贝叶斯推理

随机动力学模型通常用于描述复杂的生物过程。通常，这些模型在分析上是难以处理的，并且具有需要从观察到的数据中估计的未知参数。理想情况下，我们将对过程中所有相互作用的化学物质进行测量，并及时连续观察。然而，实际上，仅在相对较少的时间点进行测量。在某些情况下，只能对过程进行非常有限的观察，例如当实验者只能观察对活细胞比例的嘈杂观察时。这使得推理任务更加成问题。