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Boltzmann Normalization Constant/Partition Function (Discrete)

Prerequisites

Boltzmann Normalization Constant/Partition Function | \(Z = Z(T) = \int_{\mathbf{s} \in S} \exp\left\{ - \frac{ E(\mathbf{s}) }{ T } \right\} d\mathbf{s}\)

Description

The definition of the Boltzmann distribution is given by quantities of a system's microstates which can be practically unbounded (temperature \( \htmlClass{sdt-0000000029}{T} \)) or arbitrarily defined (energy \( \htmlClass{sdt-0000000100}{E} \) with multiple possible offsets). This makes the sum over the probability mass function different from 1, so a normalization constant is necessary.

\[\htmlClass{sdt-0000000077}{Z} = \htmlClass{sdt-0000000077}{Z}(\htmlClass{sdt-0000000029}{T}) = \sum_{\htmlClass{sdt-0000000091}{\mathbf{s}} \in \htmlClass{sdt-0000000026}{S} } \exp\left\{ - \frac{ \htmlClass{sdt-0000000100}{E}(\htmlClass{sdt-0000000091}{\mathbf{s}}) }{ \htmlClass{sdt-0000000029}{T} } \right\}\]

Symbols Used:

This symbol represents all possible microstates of a multi-particle system.

\( T \)

This symbol represents the temperature in a system.

\( Z \)

This symbol represents a normalizing factor for a function.

\( \mathbf{s} \)

This symbol represents a full description of the system taken at molecular level.

\( E \)

This symbol represents the energy.

Derivation

  1. Consider the partition function for the (continuous) Boltzmann probability distribution:
    \[\htmlClass{sdt-0000000077}{Z} = \htmlClass{sdt-0000000077}{Z}(\htmlClass{sdt-0000000029}{T}) = \int_{\htmlClass{sdt-0000000091}{\mathbf{s}} \in \htmlClass{sdt-0000000026}{S}} \exp\left\{ - \frac{ \htmlClass{sdt-0000000100}{E}(\htmlClass{sdt-0000000091}{\mathbf{s}}) }{ \htmlClass{sdt-0000000029}{T} } \right\} d\htmlClass{sdt-0000000091}{\mathbf{s}}\]
  2. For discrete microstate spaces \( \htmlClass{sdt-0000000026}{S} \), the distribution becomes a probability mass function and the integration becomes a sum.
  3. The same derivation given for the continuous case can be used, knowing the (discrete) probability mass function also must sum to 1. This results in:
    \[ \htmlClass{sdt-0000000077}{Z} = \sum_{\htmlClass{sdt-0000000091}{\mathbf{s}} \in \htmlClass{sdt-0000000026}{S}} \exp\left\{ - \frac{ \htmlClass{sdt-0000000100}{E}(\htmlClass{sdt-0000000091}{\mathbf{s}}) }{ \htmlClass{sdt-0000000029}{T} } \right\} \]
    as required.

References

  1. Jaeger, H. (n.d.). Neural Networks (AI) (WBAI028-05) Lecture Notes BSc program in Artificial Intelligence. Retrieved June 9, 2024, from https://www.ai.rug.nl/minds/uploads/LN_NN_RUG.pdf