Nanotechnology involves manipulating materials at the atomic and molecular scale, where traditional deterministic methods often fall short. Bayesian approaches offer several advantages:
1. Incorporation of Prior Knowledge: Bayesian methods allow researchers to include prior knowledge and experimental data, making them more flexible and informative. 2. Uncertainty Quantification: They provide a natural framework for quantifying uncertainty, which is crucial in nanoscale measurements and predictions. 3. Adaptive Learning: Bayesian algorithms can adapt as new data becomes available, making them ideal for iterative experimental designs.
