sannolikhetsmodellen
A probability model is a mathematical representation of a random phenomenon, used to describe the likelihood of different outcomes. It consists of a sample space, which is the set of all possible outcomes, and a probability measure, which assigns a probability to each outcome or set of outcomes. Probability models are fundamental in statistics, machine learning, and various fields of science and engineering.
There are two main types of probability models: discrete and continuous. Discrete models deal with countable
Probability models can be classified into different categories based on their structure and assumptions. Some common
1. Binomial model: Describes the number of successes in a fixed number of independent Bernoulli trials.
2. Poisson model: Describes the number of events occurring within a fixed interval of time or space,
3. Normal (Gaussian) model: Describes a continuous random variable that is symmetrically distributed around its mean.
4. Exponential model: Describes the time between events in a Poisson process, with the rate parameter λ.
Probability models are used to make predictions, estimate parameters, and test hypotheses. They provide a framework