Mathematical modelling and problem solving

Probability models

Here we include different kinds of models that are based on probabilities, also known as stochastic models. They can either be analyzed analytically, or be simulated with a stochastic simulation. Generally, first try to understand the problem intuitively. Draw graphs and make tables with given data. Then be systematic: variables, equations, calculations,...


    Every group will participate in a final meeting to discuss final reports and grading. Meetings will take place on Wednesday October 30 (morning and afternoon), Thursday October 31 (morning and afternoon) and Friday November 1 (morning). Clearly state when you are available, so that we can schedule your group appropriately. Please answer this question even if you do not have any constraints, so that we know this for sure.
    Please contact Birgit if none of the times fit you.


    A classical application of statistical models is simulation of systems where things happen randomly based on certain probability ditributions that are chosen to be as realistic as possible. This is called Monte Carlo simulation. See for example these simple traffic simulation demos: demo1, demo2, demo3, wikipedia (there are lots out there). I intentionally chose some simple demos, so that you can more easily relate to actually creating them yourself. As in many other areas these days, there is a lot of sophisticated software with advanced models and beautiful graphics, where you almost forget that there is the same basic mathematical techniques behind. Briefly discuss the differences in the kind of predictions you can draw from stochastic models, compared to when have a deterministic model (like for example an astronomical model of planetary motion).


    A rental car company has two offices in the cities A and B. Customers are free to leave the cars in any city, independently of where they were rented. Based on collected statistics it is known that a car rented in A is returned there with probability 0.6, and a car rented in B is returned there with probability 0.7.

    • How can you compute the probability that a single car rented in A is in A after n rentals, as a function of n? Hint: do not worry about the combinatorical complexities of what happens after a few steps. Take one step at a time, define variables etc.
    • What proportion of the cars will be in A and B in the long run?
    • Given an initial number of cars in A and B, is it possible with this model to determine the expected number of cars in A and B as a function of time? Motivate your answer and explain the solution or suggest how to extend the model.


    Try out this program that generates text as a Markov chain based on statistics from a template text. The idea is that the program generates the next letter randomly, given the k previous letters, from the distribution p(xn|xn-1,...,xn-k) estimated from the template text (Note that this is not the same as the most probable sequence!) The program works better with larger inputs, so copy and paste some text from somewhere. Try the program for different input texts (e.g. try English and Swedish), and different values of "Order" in the interval 0-5. Discuss your observations (see this as an investigation, the result is where your observations take you!). If you want to see the source code of the generator, it is available here:


    A public screening is done of a group of people to find the persons who have the disease X. This is done with a medical test. As with most medical tests, the test is not 100% reliable. It gives a correct result with a probability of 99% if the person has the disease, and with 97% if the person does not have the disease. Prior to the screening, it has been estimated that about 0.33% of the population have the disease. (Note: This means that 0.0033 of the population have the disease, not 0.33!) For a particular person the test has indicated a positive result. What is the probability that the person actually has the disease? Hint: Begin by writing down in mathematical notation what you know from the start. Try also to think what would happen with very extreme or symmetric numbers to explore and understand the problem (this is a good general trick).


    In statistical expert systems knowledge is represented with a probability distribution over all the variables. The probability distribution is defined by a Markov graph in the form of cause-effect relations (sometimes called probabilistic/belief/bayesian networks). Given the structure of the graph, the parameters can be estimated from statistical data, and inferences and predictions can be made according to the laws of probability by using Bayes theorem. This problem goes more deeply into the "Asia" example of a Bayesian network that was discussed in the introductory lecture.

    • Read this text about probabilistic expert systems. In particular, look at the definition of the Asia network on pages 20-21. (The notation used is quite old-fashioned: they write for example p(t|a) where we would write p(t=1|a=0).) Logic, Uncertainty, and Probability
    • Try out this online app "Asia". Input some observations and see how the probabilities are updated. How does the probability of "Bronchitis" change when you observe "positive X-ray"?
    • Explain mathematically how the joint probability distribution p(x1,x2,x3,x4,x5,x6,x7,x8) is defined for the Asia network. (you can give the variables other names if you think that is convenient)
    • Explain how the calculation in problem 4 can be seen as updating a prior distribution to a posterior distribution. What is the prior, the likelihood function and the posterior in this case?
    • Explain mathematically how the updated node probabilities p(x1|observation), p(x2|observation), .. can be calculated when an observation is added. From your explanation, it should be possible to clearly understand how the updated probability for "Bronchitis" was calculated when the observation "positive X-ray" was given. Hint: imagine that you already have computed p(x1,x2,x3,x4,x5,x6,x7,x8) as a single big table according to 5b, and how to update this table when new information is received. Ignore the arrows in the graph.
    • (DAT026/DIT992 students only) Compare the use of Bayesian networks with an expert system based on production rules such as CLIPS, which you investigated in module 4.
    • (Optional) Explore the demo applet and learn more. Write about what you have learned.


    Your task is to predict the probability of precipitation ("nederbörd") on a given future day. To help you, you have weather statistics from the last five years. Suppose you want to predict if there will be any precipitation on May 19. Should you base your prediction on:

    • the relative frequency of precipitation on May 19 for the last five years
    • the relative frequency of precipitation on all days in May during these years
    • the relative frequency of precipitation on all days during these years?
    Assume that the probability is simply estimated as the relative frequency of precipitation for all days you choose to include, and nothing else.Motivate your answer, and discuss the difficulties involved in choosing the model. Would the situation be any different if you had 100 years of weather statistics? Is choosing the model something that necessarily requires human judgement? Hint: while the specific question is not so very difficult to intuitively answer, the general problem behind the question is deep. It is quite possible to write a PhD thesis around this single question. So think!