How do you prove $S=-\sum p\ln p$? How does one prove the formula for entropy $S=-\sum p\ln p$?
Obviously systems on the microscopic level are fully determined by the microscopic equations of motion. So if you want to introduce a law on top of that, you have to prove consistency, i.e. entropy cannot be a postulate.
I can imagine that it is derived from probability theory for general system. Do you know such a line?
Once you have such a reasoning, what are the assumptions to it?
Can these assumptions be invalid for special systems? Would these system not obey thermodynamics, statistical mechanics and not have any sort of temperature no matter how general?
If thermodynamics/stat.mech. are completely general, how would you apply them the system where one point particle orbits another?
 A: The best (IMHO) derivation of the $\sum p \log p$ formula from basic postulates is the one given originally by Shannon:
Shannon (1948) A Mathematical Theory of Communication. Bell System Technical Journal. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6773024
However, Shannon was concerned not with physics but with telegraphy, so his proof appears in the context of information transmission rather than statistical mechanics.  To see the relevance of Shannon's work to physics, the best references are papers by Edwin Jaynes. He wrote dozens of papers on the subject.  My favorite is the admittedly rather long
Jaynes, E. T., 1979, `Where do we Stand on Maximum Entropy?' in The Maximum Entropy Formalism, R. D. Levine and M. Tribus (eds.), M. I. T. Press, Cambridge, MA, p. 15; http://bayes.wustl.edu/etj/articles/stand.on.entropy.pdf
A: The theorem is called the noiseless coding theorem, and it is often proven in clunky ways in information theory books. The point of the theorem is to calculate the minimum number of bits per variable you need to encode the values of N identical random variables chosen from $1...K$ whose probabilities of having a value $i$ between $1$ and $K$ is $p_i$. The minimum number of bits you need on average per variable in the large N limit is defined to be the information in the random variable. It is the minimum number of bits of information per variable you need to record in a computer so as to remember the values of the N copies with perfect fidelity.
If the variables are uniformly distributed, the answer is obvious: there are $K^N$ possiblities for N throws, and $2^{CN}$ possiblities for $CN$ bits, so $C=\log_2(k)$ for large N. Any less than CN bits, and you will not be able to encode the values of the random variables, because they are all equally likely. Any more than this, you will have extra room. This is the information in a uniform random variable.
For a general distribution, you can get the answer with a little bit of law of large numbers. If you have many copies of the random variable, the sum of the probabilities is equal to 1,
$$ P(n_1, n_2, ... , n_k) = \prod_{j=1}^N p_{n_j}$$
This probability is dominated for large N by those configurations where the number of values of type i is equal to $Np_i$, since this is the mean number of the type i's. So that the P value on any typical configuration is:
$$ P(n_1,...,n_k) = \prod_{i=1}^k p_i^{Np_i} = e^{N\sum p_i \log(p_i)}$$
So for those possibilities where the probability is not extremely small, the probability is more or less constant and equal to the above value. The total number M(N) of these not-exceedingly unlikely possibilities is what is required to make the sum of probabilities equal to 1.
$$M(N) \propto e^{ - N \sum p_i \log(p_i)}$$
To encode which of the M(N) possiblities is realized in each N picks, you therefore need a number of bits B(N) which is enough to encode all these possibilities:
$$2^{B(N)} \propto e^{ - N \sum p_i \log(p_i)}$$
which means that
$${B(N)\over N} =  - \sum p_i \log_2(p_i)$$
And all subleading constants are washed out by the large N limit. This is the information, and the asymptotic equality above is the Shannon noiseless coding theorem. To make it rigorous, all you need are some careful bounds on the large number estimates.
Replica coincidences
There is another interpretation of the Shannon entropy in terms of coincidences which is interesting. Consider the probability that you pick two values of the random variable, and you get the same value twice:
$$P_2 = \sum p_i^2$$
This is clearly an estimate of how many different values there are to select from. If you ask what is the probability that you get the same value k-times in k-throws, it is
$$P_k = \sum p_i p_i^{k-1}$$
If you ask, what is the probability of a coincidence after $k=1+\epsilon$ throws, you get the Shannon entropy. This is like the replica trick, so I think it is good to keep in mind.
Entropy from information
To recover statistical mechanics from the Shannon information, you are given:


*

*the values of the macroscopic conserved quantities (or their thermodynamic conjugates), energy, momentum, angular momentum, charge, and particle number

*the macroscopic constraints (or their thermodynaic conjugates) volume, positions of macroscopic objects, etc.


Then the statistical distribution of the microscopic configuration is the maximum entropy distribution (as little information known to you as possible) on phase space satisfying the constraint that the quantities match the macroscopic quantities.
A: The functional form of the entropy $S = - \sum p \ln p$ can be understood if one requires that entropy is extensive, and depends on the microscopic state probabilities $p$.
Consider a system $S_{AB}$ composed of two independent subsystems A and B. Then $S_{AB} = S_A +S_B$ and $p_{AB} = p_A p_B$ since A and B are decoupled.
$$
S_{AB} = - \sum p_{AB} \ln p_{AB} = -\sum p_{A} \sum p_B \ln p_A  -\sum p_{A} \sum p_B \ln p_B
$$
$$
 = -\sum p_{A}  \ln p_A  - \sum p_B \ln p_B = S_A + S_B
$$
This argument is valid up to a factor, which turns out to be the Boltzmann constant $k_B$ in statistical mechanics: $S = - k_B \sum p \ln p$ which is due to Gibbs, long before Shannon. 
A: Approaching this from a purely Physics perspective, this is the Gibbs entropy of a system. Firstly, although the concept of entropy can be extended we are usually discussing equilibrium thermodynamics, and this is certainly where the Gibbs entropy is first introduced.
You are of course right that technically the dynamics could be fully described by their equations of motion, but then there wouldn't really be much need for the subject of thermodynamics. I mean thermodynamics in some ways is not as "fundamental" as other subjects in physics, in that it does not try to give a complete description of everything about the system you're studying. You're usually discussing large systems (and so looking for macroscopic properties), or small systems interacting with a large environment. (for example, it doesn't make a huge amount of sense to talk about the temperature of an electron) In reality it is entirely impractical to search for a deterministic description of such systems (even without Chaos theory and quantum mechanics the number of equations would just be too enormous) and so you use thermodynamics.
With equilibrium statistical thermodynamics (which is looking for a justification of classical thermodynamics based on averages of a microscopic description), you start with the principle of equal a priori probabilities which says for an isolated system which has been left alone for along time (vague, but basically that it's in equilibrium) every microstate available to the system is equally likely to be occupied. This is a big assumption, and there are a lot of people who would like to be able to justify it properly, but it's often argued on symmetry (with the information you have there is no reason to assume one particular microstate would be more likely than any other). More than that, it just works. 
The entropy of an isolated system was then postulated to be $S=k \ ln(\Omega)$ by Boltzmann where $\Omega$ is the number of microstates available to the system (it's easier to build this up assuming a discrete number of microstates, especially if you are talking about Boltzmann/Gibbs entropy). It's a postulate, but it needs to be consistent with the classical thermodynamic entropy. The Gibbs entropy is a natural extension of this when you consider systems which are in thermal contact with an environment and the microstate probabilities are no longer equal. You can show that it is consistent with the classical thermodynamic entropy for a number of systems, and really shows how entropy can be considered to be a measure of uncertainty about the microscopic details of the system.
