{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

ch1 - Finding a cheese in the maze mouse Markov Chain Black...

Info icon This preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon
1 Markov Chain Chapter 1 2 Finding a cheese in the maze Cheese Black hole mouse 3 Assumptions The mouse does not know where the cheese is. The mouse will get nothing and leaves the game if it enters the black hole. 4 Questions A biologist may want to know the following things. Can the mouse detect the cheese by smell? Can the mouse self-train if it is put back to the game after entering the black hole? Would the chance of getting cheese be the same for different initial position?
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
5 Establish a probabilistic model To determine if the mouse can find cheese by smell, we use the following idea. Determine the probability of success under the alternative hypothesis. Conduct experiments and compare the results with the probability. If the successful rate is not higher than the theoretical probability significantly, then the hypothesis is rejected. 6 Establish a Probabilistic model Self-train hypothesis. Idea: Use special materials to make the maze so that the mouse cannot find cheese by smell. Determine the probability under the alternative hypothesis. If the successful rate is not higher than the theoretical probability significantly, then the hypothesis is rejected. 7 Model construction: ass. room # 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 8 Model construction: stochastic Define the stochastic variable X n be the position of a mouse at step n. X 0 = 0 means the mouse is initially placed in room 0. Successful probability: P(X n = 13, for some n) Failure probability: P(X n = 7, for some n)
Image of page 2