Poisson Distribution the probability mass function of X:  Homogeneous Poisson Process This process is characterized by a rate parameter λ, also known as intensity, such that the number of events in time interval (t, t + τ] follows a Poisson distribution with associated parameter λτ.  Non-homogenous Poisson Process If
the rate λ changes over time (being λ(t)) a poisson process is said to
be non-homogeneous. The expected number of events between time a and time b is as λ_a,b: the number of arrivals in the time interval (a, b], given as N(b) − N(a), follows a Poisson distribution with associated parameter λa,b counts
of the number of "events" inside each of a number of non-overlapping
finite sub-regions should each have a Poisson distribution and should be
independent of each other. |
Software Engineering ➼ Machine learning ➼ Data Science ➼ Product Leadership 🎯 > Math > Probability >