Add a running example for R

[gvwilson]

From Dave Robinson's Stochastic Processes in R:

Stochastic Processes in R

Step 1: Brainstorming

1. What problem(s) will student learn how to solve?
   • They'll learn the generative models behind several stochastic process models, and understand to simulate them with R functions.
2. What techniques or concepts will students learn?
   • In learning three new distributions, the exponential, gamma, and uniform, they'll reinforce the concept that (unlike the binomial) distributions can be continuous
   • They'll become more familiar with the use of simulation to understand probability models and answer questions about them.
3. What technologies, packages, or functions will students use?
   • The distribution-related functions around the exponential, gamma, and uniform functions
   • The sample, cumsum, replicate, and accumulate functions
4. What terms or jargon will you define?
   • Random walk, Markov chain, transition matrix, waiting time, Poisson process, Poisson point process
5. What analogies will you use to explain concepts?
   • Two people gambling repeatedly with a coin for a random walk
   • Randomly generated text in a Markov chain
   • A light bulb burning out for an exponential variable/Poisson process
   • Simulating trees in a forest for a spatial Poisson process

Step 2: Who is this course for?

Link to student profiles. If these don't match exactly, feel free to modify as needed in discussion:

• Catalina: Catalina wants to teach probability as a foundation for statistical inference, and this is perfect for such a class.
• Yngve is a great audience for the course because stochastic process form the basis of forecasting and time series methods.
• Anya is interested in web traffic data, and a Poisson process gives a good background for analyzing this data, though it may be on the more theoretical side for her.
• Thanh may already know this material from his background in statistics, though it may serve as a useful refresher (I've met students with similar background who never took a proper probability course).
• Mohan wouldn't be interested because this course doesn't give a fast solution to homework problems. Instead, it combines with other courses to build a deep understanding of statistical inference.

Step 3: How far will this course get its students?

This could be similar to the last exercise of the course or a last exercise in a chapter.

Last exercise in Chapter 2: Write a function that simulates 100 steps from a Markov chain of words, given a transition_matrix with row and column names.

Skills:

• Understand what a Markov chain is
• Understand transition matrices are, including subsetting a row and row/col names
• Use sample with prob to find the next state in a transition matrix
• Use accumulate to save up many steps in a chain

Solution:

library(purrr)

simulate_step <- function(state) {
  state <- sample(nrow(transition_matrix), 1, prob = transition_matrix[state, ])
  colnames(transition_matrix)[state]
}

accumulate(1:100, simulate_step, .init = "the")

Last exercise in the course: Write a function that, given a number of points, the width of a region, and the height of a region, generate that many points in a Poisson point process.

Use it to plot 50 points in the space of 10 x 10.

Skills required:

• Understand how a point process comes from a combination of the Poisson and the uniform, even in two dimensions
• Create a function that takes arguments (we'd provide scaffolding)
• Simulate from the Poisson distribution using rpois
• Simulate from the uniform distribution using runif, twice
• Use plot to compare and x and y (we'd give scaffolding).

Solution

simulate_points <- function(density, x_width, y_width) {
  number <- rpois(1, density * x_width * y_width)
  x <- runif(number, 0, x_width)
  y <- runif(number, 0, y_width)

  plot(x, y)
}

simulate_points(1, 10, 10)

Step 4: What will the student do along the way?

Middle of chapter 2: Generate three steps in a Markov chain given a transition matrix and starting state.

Solution:

step2 <- sample(nrow(transition_matrix), 1, transition_matrix[state, ])
step3 <- sample(nrow(transition_matrix), 1, transition_matrix[state2, ])
step4 <- sample(nrow(transition_matrix), 1, transition_matrix[state3, ])

Middle of chapter 4: Randomly generate 100 events in a one-dimensional Poisson process with a rate of 3 per second by simulating exponential waiting times. Find the distribution of how many events happen in the first 2 seconds.

Solution:

cumsum(rexp(100, 3))

replicate(1000, sum(cumsum(rexp(100, 3)) <= 2))

Step 5: How are the concepts connected? (Course outline)

Chapter 1: Random walks

• Lesson 1.1 - Random walks: Imagine you were gambling with a friend, and betting on a coin. Each time you either lose one dollar or gain one dollar. This is a random walk: at any moment, it could go up or down one step.

  • You can simulate a step with sample(), and find the cumulative position with cumsum().
  • You can plot this with plot(), which looks a bit like a stock price graph.

• Lesson 1.2 - Biased random walk: So far the random walk has been symmetrical, with an equal probability of gaining or losing.

  • What if the game were a bit rigged: each time, you had a 51% chance of losing? This is similar to playing in a casino, where the casino has an advantage we call the "house edge".
  • Try a very large number of steps: watch it "plummet". In terms of casino games, this proves the old expression that "the house always wins".

• Lesson 1.3 - Properties of a random walk: Where will a random walk end up after 10 steps? 100 steps?

  • Introduce replicate() for simulating it.
  • The end position looks like a normal distribution (bell curve), because of a mathematical result called the central limit theorem.
  • Mean and variance of the end position.

  Chapter 2: Markov chains

• Lesson 2.1 - Transition matrices

  • A random walk is one case of a Markov chain: a process where the next step depends only on the current state. Example we'll return to: Markov chain of words.
  • Creating a transition matrix, here's what it means: each cell has probability of going from a particular state to another state
  • Two multiple choice questions: what is the probability of going from state 1 to 2 with this matrix?

• Lesson 2.2 - One step in a Markov chain

  • sample(2, 1, prob = transition_matrix[state, ]) lets you randomly step
  • Try it three times in a row
  • Introduce an "absorbing" state

• Lesson 2.3 - Accumulating steps in a chain

  • Using the purrr accumulate function to add up states into a chain.
  • Notice how an absorbing state works.

• Lesson 2.4 - Example: Markov chain of words

  • We're giving you a transition matrix of words. These were determined based on a set of newspapers
  • Simulate a series of words.

Chapter 3: The exponential distribution

• Lesson 3.1 - The exponential distribution Imagine a light bulb has a random chance of burning out, but
  • Show using rexp to simulate
  • Go over the mean and the expected value
• Lesson 3.2 - The memoryless property
  • If a light bulb hasn't burned out in the first 5 days, how much longer would we expect it to last?
  • Try exp_greater_5 <- exp_sample[exp_sample >= 5], then examine the properties and distribution of exp_greater_5.
  • This is still an exponential distribution
• Lesson 3.3 - The gamma distribution
  • What if you replaced the light bulb each time? How long would it take to replace 3 lightbulbs?
  • Simulate with sum(rexp(3, 5))
  • This is the gamma distribution

Chapter 4: Poisson processes

• Lesson 4.1 - Poisson process
  • Take the exponential waiting times from the last exercise, and use cumsum
  • These are event times in what we call a Poisson process.
  • Example: website visits per hour, radioactive decay, cars driving by.
• Lesson 4.2 - The uniform distribution
  • Another way of simulating a Poisson process
  • Approach this by asking two questions in a row. How many events are in this interval (Poisson), and where are they located (uniform)?
  • The uniform distribution randomly picks points in an interval, between a minimum and maximum. ruinf.
• Lesson 4.3 - Poisson point process
  • Everything in this course was over time, but this also works in spatial simulations.
  • Simulate rabbits located in a forest. (It could also be cells under a microscope, or cities in a country). Suppose the position of each rabbit were completely independent of all the others.
  • Approach this by asking two questions in a row. How many points are in this region, and where are they located? This is a two-dimensional version of a Poisson process.

Step 6: Course overview

Course Description

Whether it's prices in the stock market, the number of visitors to a website, or the population of rabbits in a forest, many phenomena that we'd like to model with statistics involved numbers tracked over time. In this course, you'll be introduced to the field of stochastic processes, an area of probability studying systems that change over time. You'll learn about common statistical models such as random walks, Poisson processes, and Markov chains, as well as being introduced to the exponential and gamma distributions. These provide the fundamentals for many statistical methods common in finance, biology, and many other fields.

Learning Objectives

• Be introduced to statistical models often used to represent a system changing over time: random walks, Poisson processes, and Markov chains
• Learn about two new statistical distributions, the exponential and the gamma, that are important in modeling .
• Gain experience with probability concepts such as random variables, distributions, expected value, and variance
• Practice using simulation to understand and answer probability problems

Prerequisites

• Intermediate R
• Foundations of Probability in R

[richierocks]

@gvwilson Do you still want this? There are examples for each step of the speccing process, so do you need to have a complete example as well?

[gvwilson]

I don't think so, especially now that we have an example of a complete R spec.