Poisson Process Courses and Certifications

Modelling events in time

2 ratings at OpenLearn

This free course develops ideas about probability and random processes. Sections 1 and 2 introduce the fundamental ideas of random processes through a series of examples. Section 3 describes a ...

• OpenLearn
• 16 hours
• On-Demand
• Free Certificate

Probability - The Science of Uncertainty and Data

34 reviews

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference -- Part of the MITx MicroMasters program in Statistics and Data Science.

• edX
• 16 weeks, 10-14 hours a week
• On-Demand
• Free Online Course (Audit)

• Best of All Time

Queuing Theory: from Markov Chains to Multi-Server Systems

13 reviews

Learn key mathematical tools necessary to anticipate the performance levels of queueing systems and understand the behavior of other systems that evolve randomly over time.

• edX
• 5 weeks, 3-4 hours a week
• On-Demand
• Free Online Course (Audit)

Probabilistic Systems Analysis and Applied Probability

A course on the modeling and analysis of random phenomena and processes, including the basics of statistical inference.

• MIT OpenCourseWare
• 21 hours
• On-Demand
• Free Video

Discrete Stochastic Processes

This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of Discrete stochastic processes.

• MIT OpenCourseWare
• 33 hours
• On-Demand
• Free Video

Comprehensive resource for probability, statistics, and random processes, featuring textbook, video lectures, online calculators, and solutions manual for engineering and mathematics students.

• Independent
• On-Demand
• Free Online Course

METUopencouseware

Signals and Systems II

Comprehensive exploration of advanced signal processing concepts, including probability theory, random variables, stochastic processes, and spectral analysis for electrical engineering applications.

• YouTube
• 1 day 8 hours
• On-Demand
• Free Video

IIT Bombay;  NPTEL

Introduction to Stochastic Processes

Learn probability techniques to model and analyze random events in daily life, covering topics from basic probability to advanced stochastic processes and their real-world applications.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

Probability Theory and Applications

Explore advanced probability concepts, stochastic processes, and queueing models. Gain insights into Markov chains, Poisson processes, and reliability theory for practical applications.

• YouTube
• 23 hours
• On-Demand
• Free Video

Mathemaniac

Queuing Theory and Poisson Process

Introduction to M/M/1 queue model and Poisson process, covering fundamental concepts of queuing theory. Explores key principles and applications in simple queuing systems.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Hausdorff Center for Mathematics

Schramm-Steif Variance Inequalities for Poisson Processes and Noise Sensitivity

Explore Poisson processes, variance inequalities, and noise sensitivity in stochastic geometry. Gain insights into k-percolation and the Poisson Boolean model with bounded grains.

• YouTube
• 34 minutes
• On-Demand
• Free Video

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Causal Set Kinematics: Reconstructing Spacetime Geometry from Random Posets

Explore causal set quantum gravity, reconstructing spacetime geometry from random posets. Learn dimension estimation, topology, and geometric invariants in discrete orders.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Inspection Paradox Approach to Stochastic Resetting

Explore the inspection paradox and its application to stochastic resetting in random processes, from diffusion to animal foraging, using elementary mathematical tools.

• YouTube
• 46 minutes
• On-Demand
• Free Video

The Julia Programming Language

Combined Stochastic Models for Cancer Patient Trajectories

Integrating diverse clinical data into a unified stochastic model for cancer patient trajectories, combining tumor growth, metastasis, and survival data using Julia for simulation and Bayesian inference.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Toronto Machine Learning Series (TMLS)

A Cookbook for Deep Continuous-Time Predictive Models

Explore deep continuous-time predictive models for irregularly-sampled time series, from feedforward approaches to latent-variable stochastic differential equations.

• YouTube
• 47 minutes
• On-Demand
• Free Video