Combining Nested Sampling and Normalizing Flows
In order to validate our understanding of the world around us, we want to compare theoretical models to data we have actually observed. Often, these models are functions of parameters, and we want to know the values of those parameters such that the models most closely represent the world. For example, we may believe the concentration of one molecule in a chemical reaction should decrease exponentially with time. However, we also want to know the rate constant, the parameter in the model that multiplies time in the exponential, such that the model exponential curve actually resembles a specific reaction that we observe. This is the problem of parameter inference, for which we often turn to Bayesian methods, especially when working with complex models and/or many parameters..