## 2.0 Deterministic models

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

## 3.0 Stochastic modeling

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

## 3.1 Random variables & Distributions

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

## 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

## 3.3 Conditioning

Conditional probability, conditional distributions, rejecting specific samples

## 4.0 Bayesian inference

Finding model parameters using Bayesian inference

## 2.3 Expressions

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

## 2.4 Iterations and accumulators

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

## 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

## 2.1 Models and Projects in StatSim

Introduction to StatSim models

## 1.0 Overview

Overview of StatSim