Sampling Based Methods
Sampling based methods are computational techniques that use random or systematic sampling to approximate solutions to complex problems, particularly in high-dimensional spaces or where exact solutions are intractable. They are widely used in statistics, machine learning, and optimization to estimate distributions, perform inference, or explore solution spaces efficiently. Common applications include Monte Carlo simulations, Bayesian inference, and probabilistic robotics.
Developers should learn sampling based methods when dealing with problems involving uncertainty, high-dimensional data, or complex probabilistic models, such as in Bayesian machine learning, reinforcement learning, or financial modeling. They are essential for tasks like parameter estimation, risk assessment, and decision-making under uncertainty, where analytical solutions are impractical. These methods enable scalable approximations in fields like data science, AI, and computational statistics.