1. 2.0 Deterministic models

    What is modeling, deterministic models, how to design effective models

  2. 3.0 Stochastic modeling

    How to deal with uncertainty, think in probabilities and work with random variables

  3. 3.1 Random variables & Distributions

    Add random variables to your models, choose correct probability distribution, analyze random output.

  4. 3.2 Function of Random Variables

    How to use a random variable in your model, function of random variables, accumulators, using random variable as a parameter

  5. 3.3 Conditioning

    Conditional probability, conditional distributions, rejecting specific samples

  6. 4.0 Bayesian inference

    Finding model parameters using Bayesian inference

  7. 2.3 Expressions

    Make calculations with expression blocks, process arrays and tensors, conditional expressions, JavaScript.

  8. 2.4 Iterations and accumulators

    Create models that change in time, accumulate values, track changes with time-series charts.

  9. 2.2 Data

    How to load data into a model, edit it using a table view, use data as a model input, as a constant, show it in output and analyze results

  10. 2.1 Models and Projects in StatSim

    Introduction to StatSim models

  11. 1.0 Overview

    Overview of StatSim