S-sampling
Definition of S-sampling
S-sampling: S-sampling is the process of drawing a random sample from a population.
S-sampling: S-sampling is the process of drawing a random sample from a population.
Definition of Jordan Canonical Form Jordan Canonical Form: A Jordan Canonical Form is a matrix representation of a square matrix that has the property that all of the eigenvalues are real and distinct. Jordan Canonical Form is a mathematical process used to find a rational solution to a polynomial equation. A Brief Overview of Jordan…
Definition of Moving Average A moving average (MA) is a statistical measure that calculates the average value of a given set of data points over a designated amount of time. The MA is typically used to smooth out irregularities or fluctuations in the data and to help identify trends. The most common type of MA…
Definition of Database Design Database Design: Database design is the process of designing the structure of a database. This includes defining the tables, fields, and relationships between them. Why does Database Design matter? Database design is an important factor in any data science or machine learning environment. It lays the foundation for how data is…
Definition of PageRank PageRank is a link analysis algorithm created by Google co-founder Larry Page. It assigns a numerical weight to each page in order to determine its importance within the web. The algorithm is based on the idea that important pages will be linked to by other important pages.
Definition of Latin Hypercube Sampling Latin Hypercube Sampling: Latin Hypercube Sampling (LHS) is a method for constructing a point sample from a probability distribution. The most common use case for LHS is in Monte Carlo simulations, where the goal is to approximate the distribution of a function by taking repeated samples from it. In order…
Definition of Kernel Density Estimation Kernel Density Estimation: Kernel density estimation is a technique used to estimate the probability density function of a random variable. It is often used to smooth out noisy data or in cases where the exact distribution of the data is unknown. How is Kernel Density Estimation used? Kernel Density Estimation…