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
In short, variance is the measurement of the distance of a set of random numbers from their collective average value.| DeepAI
A Uniform Distribution is a distribution in which there equal probabilities across all the values in the set.| DeepAI
A Random Variable is defined as a variable whose possible values are outcomes of a random phenomenon.| DeepAI
A Probability Density Function is a statistical expression used in probability theory as a way of representing the range of possible values of a continuous random variable. The area under the curve represents the interval of which a continuous random variable will fall, and the total area of the interval represents the probability that the variable will occur.| DeepAI
A Poisson Distribution is a statistical distribution used to express the probability of a given number of events occurring within a fixed interval of time or space.| DeepAI
The normal distribution is the most important and most widely used distribution in statistics. It is sometimes called the bell curve or Gaussian distribution, because it has a peculiar shape of a bell. Mostly, a binomial distribution is similar to normal distribution. The difference between the two is normal distribution is continuous.| DeepAI
A Gaussian distribution, also known as a normal distribution, is a type of probability distribution used to describe complex systems with a large number of events.| 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
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
Pattern recognition is a technique to classify input data into classes or objects by recognizing patterns or feature similarities.| DeepAI
