A memoryless distribution is a type of probability distribution where the future probabilities are independent of the past. This means that the likelihood of an event occurring in the future does not depend on how much time has already passed. The most common example of a memoryless distribution is the exponential distribution, which is often used to model the time until an event occurs, such as the time until a radioactive particle decays. Another well-known memoryless distribution is the geometric distribution, which describes the number of trials needed to achieve the first success in a series of independent Bernoulli trials. In both cases, the defining characteristic is that the process "restarts" at any point, making the past irrelevant to future outcomes.