kokonaistodennäköisyys

Kokonaistodennäköisyys, often translated as the law of total probability, is a fundamental concept in probability theory. It describes how to calculate the probability of an event by considering all possible mutually exclusive scenarios under which that event can occur. Essentially, it states that the probability of an event A can be found by summing the probabilities of A occurring in each of these mutually exclusive scenarios, weighted by the probability of each scenario itself.

The formula for total probability is expressed as P(A) = Σ P(A|B_i) * P(B_i), where A is the event whose probability we want to find, and B_i represents a set of mutually exclusive and exhaustive events (meaning one of the B_i must occur, and they cannot occur simultaneously). P(A|B_i) is the conditional probability of event A occurring given that event B_i has occurred, and P(B_i) is the probability of event B_i occurring.

This principle is widely applicable in various fields, including statistics, machine learning, and decision theory. It allows for breaking down complex probability calculations into more manageable parts. For instance, if you want to find the probability of a certain outcome in a system with multiple possible initial states, the law of total probability helps you calculate it by considering the probability of the outcome from each state and the probability of reaching that state. It provides a systematic way to account for all relevant possibilities.