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I consider 2 models $M_0$ and $M_1$, $M_1$ being more complicated than $M_0$ in the sense that it has more parameters (I usually assume than $M_0$ is nested within $M_1$). They are respectively parametrized by $\theta_0$ and $\theta_1$. I assume that

  1. $\theta_0 \subset \theta_1$ (i.e. $M_1$ has the same parameters as $M_0$ plus extra parameters)
  2. $p(\theta_0|M_1) = p(\theta_0|M_0)$ (both models have the same priors for the parameters they have in common)

I would like to prove the following inequality:

$\forall \theta_0 \\ \langle \log p(\mathcal{D | M_0}) \rangle _{p(\mathcal{D | \theta_0, M_0})} \geq \langle \log p(\mathcal{D | M_1}) \rangle _{p(\mathcal{D | \theta_0, M_0})}$

i.e. that on average, if my data $\mathcal{D}$ are generated from $M_0$ parametrized with a given $\theta_0$, then the Bayes factor is going to favor $M_0$ over $M_1$.

Has it already been done ? Intuitively, it is an application of Occam's razor (a simpler and true model will be favored over a more complicated one), but I lack a formal proof.

Precision on the notations : $p(\mathcal{D}|M_0,\theta_0)$ is not the same as $p(\mathcal{D}|M_0)$, and I thus cannot use the positivity of the Kullback-Leibler divergence. In "$M_0,\theta_0$", I specify both the model and its parameters. In "$M_0$", I only specify the model. $p(\mathcal{D}|M_0,\theta_0)$ is the probability that the data $\mathcal{D}$ are generated from model $M_0$ with parameters $\theta_0$, while $p(\mathcal{D}|M_0)$ is the marginal likelihood over all parameters (the one we use to compute the Bayes factor) : $\int_{\theta} p(\mathcal{D}|M_0,\theta)p(\theta|M_0)$ where $p(\theta|M_0)$ is the prior of parameters under model $M_0$.

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    $\begingroup$ If I understand your notation correctly, the difference between the two expressions would be the Kullback-Leibler divergence or "relative entropy" between the models (for any fixed values of $\theta_0$ and $\theta_1$). Your inequality appears to be Gibbs' Inequality. $\endgroup$
    – whuber
    Feb 12, 2020 at 13:48
  • $\begingroup$ Sadly, it is not. $p(\mathcal{D}|M_0,\theta_0)$ is not the same as $p(\mathcal{D}|M_0)$, and I thus cannot use the positivity of the Kullback-Leibler divergence. $\endgroup$ Feb 12, 2020 at 17:47
  • $\begingroup$ Then could you please clarify the difference between "$M_0,\theta_0$" and "$M_0$"? $\endgroup$
    – whuber
    Feb 12, 2020 at 18:31
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    $\begingroup$ This cannot be proven true because there seems to be no guarantees that $p(\theta|M_0)$ won't assign $0$ probability to $\theta_0$, while $p(\theta|M_1)$ assigning non-zero probability to $\theta_0$ $\endgroup$ Feb 12, 2020 at 19:38
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    $\begingroup$ The result cannot hold in general as it depends on the choice of the priors over both models. As an extreme example. take priors degenerated at $\theta_0$. $\endgroup$
    – Xi'an
    Feb 13, 2020 at 7:59

1 Answer 1

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Here is my attempt at answering the question:

Proposition: Let $\mathcal{M}_0$ and $\mathcal{M}_1$ two nested models such that $\mathcal{M}_0 \preceq \mathcal{M}_1$. We note $\Theta_0$ and $\Theta_1$ the space of possible parameters for $\mathcal{M}_0$ and $\mathcal{M}_1$, with $\Theta_0 \subset \Theta_1$. If data generated from $\mathcal{M}_0$ and $\mathcal{M}_1$ are IID, then the following inequality holds $\forall \theta_0^* \in \Theta_0$:

\begin{equation} \label{eq:proposition1} \langle \log p(\mathcal{D}|\mathcal{M}_0) \rangle _{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)} \geq \langle \log p(\mathcal{D}|\mathcal{M}_1) \rangle _{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)} \end{equation}

If data are not IID, a sufficient condition for the inequality to hold is

\begin{equation} \label{eq:condition1} k_{\mathcal{M}_0} \log (2 \pi) - \sum_{i=1}^{k_{\mathcal{M}_0}} \langle \log (\lambda_{i}^0) \rangle _{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)} \geq k_{\mathcal{M}_1} \log (2 \pi) - \sum_{i=1}^{k_{\mathcal{M}_1}} \langle \log (\lambda_{i}^1) \rangle _{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)} \end{equation}

where

$k_{\mathcal{M}_0}$ and $k_{\mathcal{M}_1}$ are the number of independent parameters of $\mathcal{M}_0$ and $\mathcal{M}_1$;

$H_0(\hat{\theta}_0)$ and $H_1(\hat{\theta}_1)$ are the Hessian matrices of the log-likelihoods $p(\mathcal{D}|\theta_0,\mathcal{M}_0)$ and $p(\mathcal{D}|\theta_1,\mathcal{M}_1)$ expressed at their respective MLEs;

$\{\lambda^0_i\}_{1 \leq i \leq k_{\mathcal{M}_0}}$ and $\{\lambda^1_i\}_{1 \leq i \leq k_{\mathcal{M}_1}}$ are the respective eigenvalues of $-H_0(\hat{\theta}_0)$ and $-H_1(\hat{\theta}_1)$.

Proof: using the same approximation as in the derivation of the BIC for $p(\mathcal{D}|\mathcal{M}_0)$ and $p(\mathcal{D}|\mathcal{M}_1)$ yields

\begin{gather} \log p(\mathcal{D}|\mathcal{M}_0) = \log p(\mathcal{D}|\hat{\theta}_0,\mathcal{M}_0) + \log \pi(\hat{\theta}_0|\mathcal{M}_0)+ \frac{k_{\mathcal{M}_0}}{2} \log (2 \pi) - \frac{1}{2} \log (|-H_0(\hat{\theta}_0)|)\\ \log p(\mathcal{D}|\mathcal{M}_1) = \log p(\mathcal{D}|\hat{\theta}_1,\mathcal{M}_1) + \log \pi(\hat{\theta}_1|\mathcal{M}_1)+ \frac{k_{\mathcal{M}_1}}{2} \log (2 \pi) - \frac{1}{2} \log (|-H_1(\hat{\theta}_1)|) \end{gather}

Both quantities then need to be averaged over $\langle \cdot \rangle_{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)}$. Assuming

\begin{equation} \langle \log p(\mathcal{D}|\hat{\theta}_0, \mathcal{M}_0) \rangle _{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)} \approx \langle \log p(\mathcal{D}|{\theta}_0^*, \mathcal{M}_0) \rangle _{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)} \end{equation}

(i.e. that the maximum likelihood estimator $\hat{\theta}_0$ will be close to the true value $\theta_0^*$ from which data were generated) yields $\langle \log p(\mathcal{D}|\hat{\theta}_0, \mathcal{M}_0) \rangle _{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)} \geq \langle \log p(\mathcal{D}|\hat{\theta}_1, \mathcal{M}_1) \rangle _{p(\mathcal{D}| \theta_0^*,\mathcal{M}_0)}$ (under Gibbs's inequality). Furthermore, $k_{\mathcal{M}_0} \leq k_{\mathcal{M}_1}$ yields $\pi(\hat{\theta}_0|\mathcal{M}_0) \geq \pi(\hat{\theta}_0|\mathcal{M}_1)$ (these quantities do not depend on $\mathcal{D}$). The inequality is thus met for the first two terms on the right-hand side.

For the last two terms, if data are IID and if the number of data points $T$ in $\mathcal{D}$ is sufficiently large, the same approximation as in the derivation of the BIC can be made:

$$ \frac{k_{\mathcal{M}}}{2} \log (2 \pi) - \frac{1}{2} \log (|-H(\hat{\theta})|) \approx -\frac{k_{\mathcal{M}}}{2} \log (T) $$

Since $k_{\mathcal{M}_0} \leq k_{\mathcal{M}_1}$, the inequality thus holds if data generated from $\mathcal{M}_0$ and $\mathcal{M}_1$ are IID.

If data are correlated, the above approximation does not hold. However, the determinant of the Hessian (which is a symmetric matrix) can be written as the product of the eigenvalues, which finally leads to the necessary condition. This inequality can also be seen as a more general version of a result presented in the following paper using less stringent approximations :

Heavens, Alan F., T. D. Kitching, and L. Verde. "On model selection forecasting, dark energy and modified gravity." Monthly Notices of the Royal Astronomical Society 380.3 (2007): 1029-1035.

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