Stochastic Processes & Systems Modeling

Probabilistic modeling of real systems — Markov chains, queueing, reliability of stochastic systems, Monte Carlo methods.

Private cohorts & on-site
Format: 3-day (8:30 a.m.–4:30 p.m.)
Level: Advanced
Location: Scheduled on demand · on-site at your facility or a regional venue
Date(s): Not yet scheduled for open enrollment. Get notified when it is, or book it privately for your team.
Includes: Certificate of Completion · printed slide binder · take-home reference text

Get notified when this course is scheduled

One email when dates are set. Or skip the wait: run it as a private cohort, on-site at your plant.

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Faculty

Faculty details for this seminar will be announced with the full schedule.

Fees

Early: $1,895 (payment 4+ weeks ahead)
Standard: $2,095 (check/ACH) · $2,165 (card)

Group discount: $200 off per attendee for 3+ from the same organization.

Also Available

  • Corporate on-site delivery at your facility
  • Private cohort sessions
  • Digital curriculum licensing

Seminar Overview

Real engineering systems face randomness — loads, failures, arrivals, demands, and process variation. Deterministic models hide the risk that actually bites. Built from MIT OpenCourseWare's probability, stochastic-processes, and statistics curriculum, this seminar teaches the probabilistic modeling engineers need: random variables, distributions, Markov processes, queueing theory, reliability of stochastic systems, and simulation of uncertainty. It is the mathematical backbone that upgrades your reliability and warranty courses into true quantitative-system engineering.

Ideal Learner

  • Reliability and warranty engineers modeling field uncertainty
  • Systems and operations engineers analyzing queues, capacity, and demand
  • Simulation and modeling engineers building stochastic models
  • Data scientists applying probabilistic reasoning to engineering
  • Design engineers who must size systems for variable loads

Learning Objectives

  • Model engineering randomness with the correct distribution and assumptions
  • Apply Markov chains and Poisson/queueing processes to system capacity and reliability
  • Quantify uncertainty and propagate it through models
  • Build and interpret Monte Carlo simulations of engineering systems
  • Connect stochastic results to risk, warranty, and design decisions

Consulting Sessions

Seminar attendees can sign up for individual consulting sessions with the instructor. Sessions are free for registered attendees, first-come first-served — sign up when registering by calling 248-539-0473 or during the seminar.

Seminar Outline

  • Random variables, expectation, variance
  • Key distributions for engineering (an affiliated engineering firmial, Weibull, normal, Poisson, binomial)
  • Choosing the distribution behind the data
  • Stochastic processes: memory and dependence
  • Discrete- and continuous-time Markov chains
  • Stationary behavior and long-run probabilities
  • Time-to-failure as a random process
  • Systems of random components: series, parallel, redundancy
  • Linking reliability to warranty and reserve math
  • Arrival and service processes, utilization
  • MM/1 and related queueing models
  • Capacity sizing under demand variability
  • Monte Carlo for propagation of variation
  • Variance reduction and when simulation is justified
  • From simulation to decision under uncertainty
  • Probability of failure, safety margins, and tail risk
  • Interpreting stochastic results for non-statisticians
  • Defensible quantitative claims for review
  • Attendees build a stochastic model of a real system
  • Run simulation and sensitivity
  • Present the risk-based recommendation

More in Track G — Computational & Quantitative Engineering

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