Poisson Process Quant Interview Guide
Poisson process quant interview guide for event counts, rates, independent increments, exponential waiting times, examples, and mistakes.
Candidates seeing arrivals, counts, and exponential waiting-time prompts.
A Poisson process counts events over time
A Poisson process models random arrivals with a rate parameter. The number of events in an interval has a Poisson distribution under the standard assumptions.
Independent increments are key
Counts in disjoint intervals are independent in the basic process. That assumption is strong and should be checked before using the model for real arrivals.
Concrete example
If calls arrive at average rate five per hour under a Poisson model, the count in a half-hour interval has mean 2.5 and follows the corresponding Poisson count distribution.
Waiting times are exponential
In a homogeneous Poisson process, interarrival times are exponential and memoryless. This links count questions and waiting-time questions.
Common mistakes
Candidates often use a Poisson process for every arrival problem. Bursty arrivals, time-varying rates, and dependence can break the simple model.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.