Valószínségvektorok

Valószínűségvektorok is a Hungarian term that translates to "probability vectors" in English. A probability vector is a vector where each element represents a probability, and the sum of all elements equals one. These vectors are fundamental in various fields, including statistics, machine learning, and computer science.

In essence, a probability vector is a way to represent a probability distribution over a discrete set of outcomes. For example, if we have three possible outcomes, A, B, and C, a probability vector could be [0.2, 0.5, 0.3], indicating that the probability of outcome A is 0.2, outcome B is 0.5, and outcome C is 0.3. The sum 0.2 + 0.5 + 0.3 = 1 confirms it's a valid probability vector.

These vectors are used in many algorithms. In machine learning, they often represent the output of a classification model, indicating the likelihood of an input belonging to each class. In quantum mechanics, probability vectors are used to describe the state of a quantum system. The mathematical properties of probability vectors, such as their non-negativity and the sum of their components, make them suitable for representing uncertain events and their associated likelihoods.