The Law of Large Numbers is a theorem within probability theory that suggests that as a trial is repeated, and more data is gathered, the average of the results will get closer to the expected value. As the name suggests, the law only applies when a large number of observations or tests are considered.| DeepAI
Central Limit Theorem states that the distribution of observation means approaches a normal distribution model as the sample size gets larger.| DeepAI
The exponential distribution, also known as the negative exponential distribution, is a probability distribution that describes time between events in a Poisson process.| DeepAI
A discrete random variable is a random variable with a limited and countable set of possible values.| DeepAI
Binomial distribution is the sum of all successes in repeated independent trials conducted on an infinite, identical population.| DeepAI
Probability Theory describes probabilities in terms of a probability space, typically assigning a value between 0 and 1, known as the probability measure, and a set of outcomes known as the sample space.| DeepAI
A Probability Distribution is the sum of the probabilities of the events occurring. There are two distinct types of probability distributions, continuous and discrete.| DeepAI
In simple words, Natural Language Processing is a field which aims to make computer systems understand human speech. NLP is comprised of techniques to process, structure, categorize raw text and extract information.| DeepAI
A decision tree is a supervised learning technique that has a pre-defined target variable and is most often used in classification problems.| DeepAI
A field of computer science that aims to teach computers how to learn and act without being explicitly programmed.| DeepAI
Bayesian inference refers to the application of Bayes’ Theorem in determining the updated probability of a hypothesis given new information.| DeepAI
