Index: The Book of Statistical ProofsModel Selection ▷ Bayesian model selection ▷ Family evidence ▷ Approximation of log family evidences

Theorem: Let $m_1, \ldots, m_M$ be $M$ statistical models with log model evidences $\mathrm{LME}(m_1), \ldots, \mathrm{LME}(m_M)$ and belonging to $F$ mutually exclusive model families $f_1, \ldots, f_F$.

1) Then, the log family evidences can be approximated as

\[\label{eq:LFE-approx-v1} \mathrm{LFE}(f_j) = \mathrm{L}^{*}(f_j) + \log \left[ \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i)] \cdot p(m_i|f_j) \right]\]

where $\mathrm{L}^{*}(f_j)$ is the maximum log model evidence in family $f_j$, $\mathrm{L}’(m_i)$ is the difference of each log model evidence to each family’s maximum and $p(m_i \vert f_j)$ are within-family prior model probabilities.

2) Under the condition that prior model probabilities are equal within model families, the approximation simplifies to

\[\label{eq:LFE-approx-v2} \mathrm{LFE}(f_j) = \mathrm{L}^{*}(f_j) + \log \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i)] - \log M_j\]

where $M_j$ is the number of models within family $f_j$.

Proof: The log family evidence is given in terms of log model evidences as

\[\label{eq:LFE-LME} \mathrm{LFE}(f_j) = \log \sum_{m_i \in f_j} \left[ \exp[\mathrm{LME}(m_i)] \cdot p(m_i|f_j) \right] \; .\]

Often, especially for complex models or many observations, log model evidences are highly negative, such that calculation of the term $\exp[\mathrm{LME}(m_i)]$ in modern computers will give model evidences as zero, making calculation of LFEs impossible.

1) As a solution, we select the maximum LME within each family

\[\label{eq:LME-max} \mathrm{L}^{*}(f_j) = \max_{m_i \in f_j} \left[ \mathrm{LME}(m_i) \right]\]

and define differences between LMEs and maximum LME as

\[\label{eq:LME-diff} \mathrm{L}'(m_i) = \mathrm{LME}(m_i) - \mathrm{L}^{*}(f_j) \; .\]

In this way, only the differences $\mathrm{L}’(m_i)$ need to be exponentiated. If such a difference is highly negative, this model’s contribution to the LFE will be zero – making this an approximation. However, the model is also much less evident that the family’s best model in this case – making the approximation acceptable.

Using the relation \eqref{eq:LME-diff}, equation \eqref{eq:LFE-LME} can be reworked into

\[\label{eq:LFE-approx-v1-qed} \begin{split} \mathrm{LFE}(f_j) &= \log \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i) + \mathrm{L}^{*}(f_j)] \cdot p(m_i|f_j) \\ &= \log \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i)] \cdot \exp[\mathrm{L}^{*}(f_j)] \cdot p(m_i|f_j) \\ &= \log \left[ \exp[\mathrm{L}^{*}(f_j)] \cdot \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i)] \cdot p(m_i|f_j) \right] \\ &= \mathrm{L}^{*}(f_j) + \log \left[ \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i)] \cdot p(m_i|f_j) \right] \; . \end{split}\]

2) Under uniform within-family prior model probabilities, we have

\[\label{eq:PMP-uni} p(m_i|f_j) = \frac{1}{M_j} \quad \text{for all} \quad m_i \in f_j \; ,\]

such that the approximated log family evidences becomes

\[\label{eq:LFE-approx-v2-qed} \begin{split} \mathrm{LFE}(f_j) &= \mathrm{L}^{*}(f_j) + \log \left[ \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i)] \cdot \frac{1}{M_j} \right] \\ &= \mathrm{L}^{*}(f_j) + \log \left[ \frac{1}{M_j} \cdot \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i)] \right] \\ &= \mathrm{L}^{*}(f_j) + \log \sum_{m_i \in f_j} \exp[\mathrm{L}'(m_i)] - \log M_j \; . \end{split}\]

Metadata: ID: P415 | shortcut: lfe-approx | author: JoramSoch | date: 2023-09-15, 16:33.