Network sampling : Network sampling is a type of sampling method that is used in research to study the characteristics and behavior of a particular group or population within a social network. This method involves selecting a sample of individuals from a larger network, and collecting data on their relationships and interactions with other members […]

Network : A network is a group of interconnected devices that are able to communicate with one another. This can be a small group of computers within a home or office, or it can be a large network of devices that spans across the globe. There are several types of networks, including local area networks […]

Nested design : Nested design, also known as hierarchical design or multilevel design, is a type of experimental design in which the levels of an independent variable are nested within each other. This means that the levels of the independent variable are not independent of one another, but rather they are related in some way. […]

Nested case-control study : A nested case-control study is a type of observational study that involves selecting a subgroup of individuals from a larger cohort and comparing their characteristics and exposures to a group of controls. This design is often used when it is impractical or costly to follow the entire cohort for a long […]

Natural Language Processing (NLP) : Natural Language Processing (NLP) is a subfield of artificial intelligence and linguistics that deals with the interaction between computers and human languages. It is concerned with how computers can understand, interpret, and generate human language in order to perform various tasks. NLP has a wide range of applications, including text […]

Naor’s distribution : Naor’s distribution is a mathematical concept that describes the distribution of certain types of random variables. It was introduced by Moni Naor in 1992 as a way to model the distribution of certain types of random variables that have a skewed distribution. One example of a random variable that follows a Naor […]

Naive Bayes : Naive Bayes is a type of machine learning algorithm that is based on the principle of Bayes’ theorem. It is called “naive” because it makes the assumption that all features (variables) in a dataset are independent of each other, which is often not the case in real-world data. Despite this assumption, naive […]

Nadaraya-Watson estimator : The Nadaraya-Watson estimator is a nonparametric method for estimating the regression function in a supervised learning problem. It is named after the Russian mathematician Alexander Nadaraya and the English statistician John Watson, who developed the method independently in the mid-1960s. The basic idea behind the Nadaraya-Watson estimator is to estimate the regression […]