Hierarchical Bayesian Modelling of Piecewise Continuous Functions using Experimental Data with Error

Music City Center 

I apply a propensity & oligomer size. SEC-MALS is a useful & inexpensive technique for studying molecular systems but the data has complex error structure that poses varied challenges. This includes the need to integrate data from different measurement types, to model elution curves nonparametrically for fractionated samples & to account for correlated error structure of weakly known concentration values in multiple parts of the measurement problem. Bayesian answers are ideal here due to strong prior knowledge of many of the unknown physical quantities, the directness of handling complex error structure, and the need for careful uncertainty quantification of quantities whose experimental precision is likely to be limited. I provide a Bayesian model for this setting, discussing specification, posterior sampling, and adequacy checks. I demonstrate this model by application to data on human gamma-S crystallin, a structural protein of the eye lens whose aggregation leads to cataract.

Bayesian hierarchical model

Flow-mode data

Gamma-s crystallin protein

Complex error structure

Measurement error 

Section on Bayesian Statistical Science