Data Driven Statistical Models vs Process Driven Physical Models

In datascience, it is becoming increasingly common to employ data driven models where process based physical models may not fully describe the processes in operational situations. In some cases, a hybrid of physical and statistical models may be required, to solve certain problems.

Models are useful tools to understand the behaviours and processes in the real world, and to make inferences about the future. In science, there are essentially two modelling approaches: 1) data driven models; and 2) process based models.

Data Driven Models

The data driven models build relationships between input and output data, without worrying too much about the underyling processes, using statistical/machine learning techniques. On the other hand, the process driven models are based on well established mathematical/physical laws.

A linear regression model is an example of a data driven model that, for example, builds a relationship between a dependent variable and a set of independent variables. This regeression model can then be used to understand the relationship between the variables, and in some cases can also be used to make predictions.

Language Translators are built purely by a data-driven approach. For example, Google Translate does…