1. The Monte Carlo Method/ Markov Chains/ Metropolitan Algorithm from sec. 2.9-2.11 in “Adaptive Cooperative Systems” -summarized by Jinsan Yang

2. 2.9 The Monte Carlo Method Definition  Monte Carlo method is a device for studying stochastic models  Introduced by Ulam and von Neumann in 1947 for solving integral and differential equations  It is easier to solve numerically to an equation by finding stochastic process and computing the resulting statistics than to solve the equation directly  The use of a random sampling method for the solution of a deterministic mathematical problem

3. Study of Reaction Processes  Monte Carlo method for the oxygen-carbon reactions  Emission probabilities for six particle species were computed and compared to a uniformly distributed random number to determine which particle type would be emitted  Candidate energy was accepted if the probability for emitting a particle with the proposed energy was greater than another random number, else was rejected and these process was repeated until an energy was selected

4. Problems in Statistical Mechanics  The Monte Carlo method was extended by Metropolis et al in 1953 to statistical mechanics to determine properties of substances of multiple interacting elements  In direct manner, every possible configuration of the N- molecule system should be included.  In Monte Carlo method, the summation is carried out by choosing a configuration at random and weight by appropriate Boltzmann factor  In the approach of Metropolis et al, a molecule was selected and a small displacement was conducted by a uniformly drawn random number. If the corresponding energy change was greater than a random number, the change was permitted and the procedure was repeated.

5. 2.10 Markov Chains Definitions  In an array of N elements, each element can be up/down state and for each unique configuration of the array corresponds a random variable: energy or magnetization of the system.  Collection of configuration points for X=x i is referred to as the state X=x i  Can extend these definitions to two or more random variables

6.  Transition Probabilities  Markov chain

7. Convergence to stationary distributions  Irreducible MC: if there is no closed collection other than the collection of all states (every state can be reached from every other states)  A probability distribution {w i } is stationary if  Convergence theorem for irreducible and aperiodic MC: n-step transition probabilities will approach a unique stationary distribution if stationary distribution exists (influence of initial state will disappear)  (stationary distribution does not exist)

8. Convergence to stationary distributions  condition of detailed balance relation :  Under above condition, the probability distribution is stationary  Goals of metropolis et al. Avid explicit evaluation of the partition function Efficient MC sampling by picking configuration relative to the importance To achieve these, transition probabilities are defined with balance relation and with Gibbs distribution as the stationary distribution

9. 2.11 The Metropolis Algorithm MC with three priori transition probability conditions: Define transition probability Gibbs distribution

10. Transition probabilities obey the detailed balance relation Limiting stationary distribution reached by the n-step transition probabilities is unique and therefore the stationary distribution generated by the Metropolis algorithm must be the Gibbs distribution.

11. Metropolis sampling procedure  At time t, the state X takes the value x t, and select a state Y t at random from the target distribution in a manner which satisfies the symmetry condition  Energy difference  If the energy difference is negative, transition is allowed  If the energy difference is positive, select a random number r between 0 and 1 and if allow the transition