Statistics

Confusion matrix is a very popular measure used while solving classification problems. It can be applied to binary classification as well as for multiclass classification problems.

A Poisson Process is a stochastic model for discrete events where the average time between events is known, but the exact timing of events is random. The arrival of an event is independent of the event before, this means that waiting time between events is memoryless.

A Brownian motion with drift and volatility is a process with directional tendency, we can think of an additive component resembling a slope growing over time. In mathematical terms it means we have some constant, let’s say multiplying the time variable.

A Brownian bridge can be defined as standard Brownian motion conditioned on hitting zero at a fixed future time T, or as any continuous process with the same distribution as this. Rather than conditioning, a slightly easier approach is to subtract a linear term from the Brownian motion, chosen such that the resulting process hits zero at the time T.

Brownian motion is one of the most important random process. It has been used in engineering, finance, and physical sciences. It is a Gaussian random process and it has been used to model motion of particles suspended in a fluid, percentage changes in the stock prices, etc.

Pearson Random Walk simulation in Python.

Simulation of some basic stochastic processes in Python such as simple random walk, Brownian motion, Brownian bridge, Brownian motion with drift and volatility, and Geometric Brownian motion.

Simple Random Walk simulation in Python.