Partition function (mathematics): Difference between revisions

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== Information geometry ==

The points <math>\beta</math> can be understood to form a space, and specifically, a [[manifold]]. Thus, it is reasonable to ask about the structure of this manifold; this is the task of [[information geometry]].

Multiple derivatives with regard to the lagrange multipliers gives rise to a positive semi-definite [[covariance matrix]]

:<math>g_{ij}(\beta) = \frac{\partial^2}{\partial \beta^i\partial \beta^j} \left(-\log Z(\beta)\right) =

\langle \left(H_i-\langle H_i\rangle\right)\left( H_j-\langle H_j\right)\rangle</math>

This matrix is positive semi-definite, and may be interpreted as a [[metric tensor]], specifically, a [[Riemannian metric]]. Equiping the space of stateslagrange multipliers with a metric in this way turns it into a [[Riemannian manifold]].<ref>Gavin E. Crooks, "Measuring thermodynamic length" (2007), [http://arxiv.org/abs/0706.0559 ArXiv 0706.0559]</ref> The study of such manifolds is referred to as [[information geometry]]; the metric above is the [[Fisher information metric]]. Here, <math>\beta</math> serves as a coordinate on the manifold. It is interesting to compare the above definition to the simpler [[Fisher information]], from which it is inspired.

That the above defines the Fisher information metric can be readily seen by explicitly substituting for the expectation value: