Diffusion Quanutm Montecarlo

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Discussion Overview

The discussion revolves around the application of diffusion quantum Monte Carlo methods, specifically focusing on the use of Gaussian distributions in the movement of Monte Carlo walkers as described in a script extract. Participants explore the implications of the propagator and its role in simulating dynamics, as well as the integration process involved in Monte Carlo simulations.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant questions why the movement of walkers is based on a Gaussian distribution, referencing a specific formula from the script.
  • Another participant explains that the propagator gives the probability of a particle's position change, which is inherently Gaussian, suggesting that simulating this with a Gaussian distribution is sensible, although other distributions could be used with a rejection step.
  • A participant expresses uncertainty about the original question but acknowledges the role of the propagator in sampling and mentions the potential use of the Metropolis algorithm.
  • Further clarification is provided regarding the integral associated with the propagator, with a participant noting that the error scales quadratically with the time step and questioning the arbitrary nature of the initial density function.
  • Discussion includes the concepts of ergodicity and detailed balance as conditions for convergence to equilibrium in Monte Carlo simulations, with a suggestion that the initial state may not significantly impact the results.

Areas of Agreement / Disagreement

Participants express differing levels of understanding regarding the original question and the specifics of the Monte Carlo process. There is no clear consensus on the implications of the propagator or the nature of the initial density function, indicating ongoing debate and exploration of these concepts.

Contextual Notes

Participants highlight the complexity of the Monte Carlo methods and the assumptions involved, such as the nature of the initial density function and the conditions for convergence, which remain unresolved in the discussion.

Derivator
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Hi,

below, you will find an extract from a script.

One sentence is:

"Move these walkers according to the the Gaussian distribution ..." (you can find this on the second image)

My question:

Why is it obvious from formula (124) (the short time step proppagator) that we have to move the walkers according to the Gaussian distribution?

http://img12.imageshack.us/img12/769/dqmc1.png

http://img3.imageshack.us/img3/7313/dqmc2.png
 
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I'm not an expert in Monte Carlo moves that are inspired by actual dynamics. Anyways: The propagator (equation 128, not 124) gives you the probability that a particle which was at x at t=0 is at y at t=delta t. This is the dynamics you want to simulate. The propagator is a Gaussian in the spatial coordinates. So it seems very sensible to me to simulate this Gaussian probability distribution by using a Gaussian probability distribution for the move from x to a random new y (sidenote: you can also propose moves from a different probability distribution, but then you'd have to reject some of those proposals). The other factor in the propagator is treated in an extra step (I think missing sentence is "If q is greater than 1 the walker survives"), and in a less direct manner.
I'm not sure if I really understood your question, though. It seems a bit strange to ask why one simulates a Gaussian via a Gaussian (even though it is a good question once you dig a bit deeper), so maybe I missed your point.
 
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Timo said:
I'm not sure if I really understood your question, though.

well, i think you did. i was not aware of, that the propagator gives the probability that a particle which was at x at t=0 is at y at t=delta t.

If I got it right, the propagator G is sampled via the Monte Carlo walkers (I assume one uses the Metropolis algorithm) and in a second step the integral right below formula 128 is calculated (via monte carlo integration?). I assume, that the initial density function \rho(y,t) in this integral is arbitrary?
 
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Hi Derivator,

sorry for the late reply. I don't have time to invest into forum discussions, so I can't offer you super thought-through comments. Still, I should at least give some feedback, I think:
Derivator said:
If I got it right, the propagator G is sampled via the Monte Carlo walkers (I assume one uses the Metropolis algorithm)
I'd rather call it sampling a process according to the dynamics given by the propagator.
and in a second step the integral right below formula 128 is calculated (via monte carlo integration?)
That's not said in the part of the text you quoted, I think. I think the formula directly below (128) merely is the claim that the error (whatever that may be in detail) scales quadratically with the time step (note that you sample small time steps).
I assume, that the initial density function \rho(y,t) in this integral is arbitrary?
In the theory of Monte Carlo simulations, there are two conditions which (in theory) guarantee that a process started from any arbitrary starting state will converge towards equilibrium, and from thereon sample states according to the equilibrium distribution. Those are ergodicity and detailed balance. I don't understand the specific process you are describing well enough to make comments about them in this particular case. But I think you can assume that this is supposed to sample an equilibrium case, and that the author of the text knows how to construct Monte Carlo algorithms => the starting state probably doesn't matter.
 

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