How Probable is the Most Probable Distribution in Statistical Mechanics?

In summary, the most probable distribution is found by using the laws of physics and the binomial distribution is a result of the number of ways a system can be organized.
  • #1
Gayle
8
0
in stat mechanics we derive most probable distribution .but this does not say any thing about existence of other less probable distributions. is there a way to find out how probable is the most probable
 
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  • #2
I come to question about these things a lot and I totally agree with what you say and there is a way to find the most propable propability, even though we aren't aware of where stuff is and we don't know their state for sure, we don't know that no matter what they wouldn't (and shouldn't) smash a bunch of laws of physics and the distribution that applies this MIGHT bd the best propability distribution, but for the best not so sure because we cannot know pricesly the state and properly describe the microstates of each little particle (example) this is an artefact of our ignorance !
 
  • #3
The distribution is derived from the laws of physics. To get a different distribution you need to get a different set of physics laws.
 
  • #4
Gayle said:
in stat mechanics we derive most probable distribution .but this does not say any thing about existence of other less probable distributions. is there a way to find out how probable is the most probable
It is a probability question. The probability is proportional to number of ways distribution can be realized.
 
  • #5
Most introductory SM books state without much back-up that the most probable distribution of energy states in a large ensemble of systems is so overwhelmingly the most probable that we can forget about the others. More precisely, they say that for a very large number of systems the logarithm of the number of ways of achieving the most probable distribution is the same as the logarithm of the sum of the numbers of ways of achieving every distribution!

Usually they refer to the binomial distribution for back-up, without properly explaining why the binomial distribution is related to the matter in hand.

I've found the following elementary example useful for clarifying what's going on…

Take an ensemble of N 3-level systems. Let the levels be non-degenerate with energies 0, E, 2E. Let the total ensemble energy be [itex]\frac{4}{7}NE[/itex]. Suppose n1 systems are on the lowest level, n2 systems are on the middle level, n3 systems are on the top level. It's easy to express n1 and n2 in terms of n3 (and the constant, N). So there's only one free variable, n3.
For this simple system it's possible to show by quite elementary means (using Stirling's formula and second order Taylor expansion) that, as N approaches infinity, the most probable distribution is the only one that carries any weight.

There's nothing special about the choice of [itex]\frac{4}{7}NE[/itex] for the total ensemble energy; it just makes the arithmetic slightly neater then many other choices. For example, in the most probable distribution n2 turns out be half n1 and twice n3.
 
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  • #6
Gayle. It would be nice to know if any of the responses to your question were of any use. If any were impossible to understand, or didn't go far enough towards answering your question, you can ask for clarification.
 

Related to How Probable is the Most Probable Distribution in Statistical Mechanics?

1. What is a probability distribution?

A probability distribution is a way of representing the likelihood of different outcomes or events occurring in a given situation or experiment. It assigns a numerical value to each possible outcome, with the total sum of all values equaling 1.

2. What is the difference between discrete and continuous probability distributions?

A discrete probability distribution is one in which the possible outcomes are countable and finite, such as the number of heads when flipping a coin. A continuous probability distribution is one in which the possible outcomes are infinite and can take on any value within a given range, such as the height of individuals in a population.

3. How is a probability distribution calculated?

A probability distribution can be calculated by either theoretical or empirical methods. Theoretical calculations involve using mathematical formulas and assumptions to determine the probabilities of each outcome. Empirical calculations involve collecting data and using statistical methods to analyze the frequency of each outcome.

4. What is the importance of understanding probability distributions?

Probability distributions are important in many fields of science, including statistics, physics, and biology. They allow us to make predictions and draw conclusions based on the likelihood of different outcomes. Understanding probability distributions also helps us to make informed decisions in situations where uncertainty is present.

5. How can probability distributions be used in real-life applications?

Probability distributions have a wide range of real-life applications, such as in risk assessment, stock market analysis, and medical research. They can also be used to model and predict natural phenomena, such as weather patterns, and to improve decision-making in various industries, including finance and marketing.

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