Adiabatic approximation for joint probability distribution

In summary, this technique is used to approximate a joint probability distribution of two ever-fluctuating spatial variables. It is found in quantum physics, but can be explained to someone with a basic understanding of applied math.
  • #1
nigels
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Hi group, I'm a theoretical ecologist with fairly adequate training in applied math (ODE, linear algebra, applied probability, some PDEs). In my current work, I've encountered the use of adiabatic approximation to a joint probability distribution of two ever-fluctuating spatial variables. A search on the web shows that this method is primarily found in quantum physics, a field I'm wholly ignorant of. Is there any document/textbook one would recommend that can explain the technique to me given my background? Of course, it'd also be fantastic if someone can clarify the concept for me themselves here on the board.

THANKS!
 
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  • #2
I speculate that you won't get a useful answer until you pose a specific problem.

The combination of the term "adiabatic" with "joint probability distribution" is so curious that I couldn't resist looking it up on the web. The simple explanation that I found ( http://www.google.com/url?sa=t&rct=...sg=AFQjCNGjHL56nLD-LA26oZ3ectFJFwCt0Q&cad=rja ) makes the technique sound disappointing. If you have a differential equation involving time with variables that vary quickly with time and some variables that only vary slowly with time, then treat the slow varying ones as constants, get the answer, put the time dependence back by making the slow varying variables functions of time again. Is that all it amounts to?
 
  • #3
Hi Stephen,

What you provided is exactly the explanation I need! And yes, the technique makes sense in this case since the model is assuming one of the two spatial variables (movement of the animal) to be diffusive and the other one (territory border of said animal) subdiffusive. The link you've provided, albeit heavily quantum physicsy, is quite helpful in painting an intuitive picture of the effect of relative time scale on the solution. Thank you so much for the wonderful help! It has made a significant difference.

Cheers.
 

Related to Adiabatic approximation for joint probability distribution

What is the adiabatic approximation for joint probability distribution?

The adiabatic approximation is a method used in quantum mechanics to simplify the calculation of joint probability distributions. It assumes that the system is changing slowly enough that the probability distribution remains constant at each step, allowing for a simpler calculation.

How is the adiabatic approximation different from other methods of calculating joint probability distributions?

The adiabatic approximation differs from other methods in that it specifically takes into account the rate of change of the system. Other methods may assume a constant rate of change or make other simplifying assumptions.

What are the limitations of the adiabatic approximation for joint probability distribution?

The adiabatic approximation can only be used for systems that change slowly, and may not accurately predict the behavior of systems that change rapidly. It also assumes a simple form for the probability distribution, which may not always be accurate.

When is it appropriate to use the adiabatic approximation for joint probability distribution?

The adiabatic approximation is most appropriate for systems that change slowly and have a simple form for the probability distribution. It is commonly used in quantum mechanics for systems with a large energy gap between the initial and final states.

How does the adiabatic approximation affect the accuracy of calculations?

The adiabatic approximation can simplify calculations and make them more manageable, but it may also introduce some errors. The accuracy of the calculations depends on the specific system and how closely it follows the assumptions of the adiabatic approximation.

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