E-Values

2 E-variables

Notation

Let \(\mathcal{X}, \mathcal{Y}\) be measurable spaces. We denote by \(\mathcal{P}(\mathcal{X})\) the set of probability measures on \(\mathcal{X}\) and write \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) to mean that \(\kappa \) is a Markov kernel from \(\mathcal{X}\) to \(\mathcal{Y}\). \(\mathbb {R}_{+,\infty }\) is the set of nonnegative reals extended with \(+\infty \) and \(\overline{\mathbb {R}}\) is the set of reals extended with \(-\infty \) and \(+\infty \).

A measure will always denote a nonnegative measure. We will specify when we mean a signed measure.

The central object of this study are E-variables, which are measurable functions that are bounded in expectation by \(1\) for all probability measures in a set \(\mathcal{Q}\).

Definition 2.1
#

An E-variable for a set of probability measures \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) is a measurable function \(f : \mathcal{X} \to \mathbb {R}_{+,\infty }\) such that for all \(Q \in \mathcal{Q}\), \(Q[f] \le 1\).

We denote by \(\mathcal{E}_{\mathcal{Q}}\) the set of E-variables for a set of probability measures \(\mathcal{Q}\).

Lemma 2.2
#

If \(\mathcal{P} \subseteq \mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) then \(\mathcal{E}_{\mathcal{Q}} \subseteq \mathcal{E}_{\mathcal{P}}\).

Proof

We introduce randomized E-variables, which are Markov kernels that can be seen as randomized versions of E-variables. The main difference is that they are allowed to be randomized, i.e., they can return a distribution over \(\mathbb {R}_{+,\infty }\) instead of a single value. This generalization is mainly useful thanks to the compositional properties it provides, as we will see in Lemma 2.22.

Definition 2.3
#

A randomized E-variable for a set of distributions \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) is a Markov kernel \(\eta : \mathcal{X} \rightsquigarrow \mathbb {R}_{+,\infty }\) such that for all \(Q \in \mathcal{Q}\), \((\eta \circ Q)[\operatorname{id}] \le 1\).

We denote by \(\mathcal{E}^R_{\mathcal{Q}}\) the set of randomized E-variables for a set of probability measures \(\mathcal{Q}\). Since measurable functions can be seen as deterministic Markov kernels, we have \(\mathcal{E}_{\mathcal{Q}} \subseteq \mathcal{E}^R_{\mathcal{Q}}\). Indeed, \(Q[f] = (\delta _f \circ Q)[\operatorname{id}]\), in which \(\delta _f : \mathcal{X} \rightsquigarrow \mathbb {R}_{+,\infty }\) is such that \(\delta _f(x)\) is the dirac distribution at \(f(x)\).

Lemma 2.4

A Markov kernel \(\eta : \mathcal{X} \rightsquigarrow \mathbb {R}_{+,\infty }\) is a randomized E-variable for a set of probability measures \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) if and only if the measurable function \(f : \mathcal{X} \to \mathbb {R}_{+,\infty }\) defined by \(f(x) = \eta (x)[\operatorname{id}]\) is an E-variable for \(\mathcal{Q}\).

Proof

2.1 Almost everywhere with respect to a set of measures

Definition 2.5
#

Let \(\mathcal{Q} \subseteq \mathcal{M}(\mathcal{X})\) be a set of measures. A set \(S \subseteq \mathcal{X}\) is said to be \(\mathcal{Q}\)-null if \(Q[S] = 0\) for all \(Q \in \mathcal{Q}\). A property \(p : \mathcal{X} \to \mathrm{Bool}\) is said to hold \(\mathcal{Q}\)-almost everywhere (denoted by \(\mathcal{Q}\)-a.e.) if the set \(\{ x \mid \neg p(x)\} \) is \(\mathcal{Q}\)-null.

For a measure \(P \in \mathcal{M}(\mathcal{X})\), we can define the almost everywhere filter \(\operatorname{ae}_P\) of sets with \(P\)-null complement.

Lemma 2.6
#

A set \(S \subseteq \mathcal{X}\) has a \(\mathcal{Q}\)-null complement iff \(S \in \bigsqcup _{Q \in \mathcal{Q}} \operatorname{ae}_Q\).

Proof

By definition of the \(\operatorname{ae}\) filter and of a supremum of filters.

Definition 2.7

Let \(P \in \mathcal{M}(\mathcal{X})\) and let \(\mathcal{Q} \subseteq \mathcal{M}(\mathcal{X})\) be a set of measures. We say that \(P\) is absolutely continuous with respect to \(\mathcal{Q}\) (denoted by \(P \ll \mathcal{Q}\)) if all \(\mathcal{Q}\)-null sets are \(P\)-null sets.

Let \(P \in \mathcal{M}(\mathcal{X})\) and let \(\mathcal{Q} \subseteq \mathcal{M}(\mathcal{X})\). Then \(P \ll \mathcal{Q}\) iff \(\operatorname{ae}_P \le \bigsqcup _{Q \in \mathcal{Q}} \operatorname{ae}_Q\).

Proof

For \(P \in \mathcal{M}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{M}(\mathcal{X})\), there is a unique decomposition \(P = P_{\mathrm{ac}} + P_{\perp }\) with \(P_{\mathrm{ac}} \ll \mathcal{Q}\) and \(P_{\perp } \perp \mathcal{Q}\) (meaning that there exists a \(\mathcal{Q}\)-null set \(A\) with \(P_{\perp }(A^c) = 0\)).

Proof
Definition 2.10
#

The singular set of \(P\) with respect to \(\mathcal{Q}\) is a \(\mathcal{Q}\)-null set \(A\) such that the singular part \(P_{\perp }\) satisfies \(P_{\perp }(A^c) = 0\).

Lemma 2.11

The singular set of \(P\) with respect to \(\mathcal{Q}\) is unique up to \(P\) and \(\mathcal{Q}\)-null sets.

Proof

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and let \(f\) be an E-variable for \(\mathcal{Q}\). Then the set \(\{ f = \infty \} \) is \(\mathcal{Q}\)-null.

Proof

Let \(Q \in \mathcal{Q}\). Since \(f\) is an E-variable for \(\mathcal{Q}\), we have \(Q[f] \le 1\). Hence \(Q[\{ f = \infty \} ] = 0\).

2.2 Utility and maximal utility

Definition 2.13
#

We call utility a function \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) which is non-decreasing, concave, finite except perhaps at 0 and \(\infty \), continuous, and continuously differentiable on \((0, \infty )\).

Definition 2.14

The logarithm satisfies the properties of a utility function and we call it logarithmic utility.

Definition 2.15

For a utility function \(U\), the maximal utility for \((P, \mathcal{Q})\) is defined as

\begin{align*} \mathcal{U}(P, \mathcal{Q}) & = \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[U(f)] \: , \end{align*}

in which the expectation is the extended integral defined in Definition 1.4.

Definition 2.16

For a utility function \(U\), the maximal randomized utility for \((P, \mathcal{Q})\) is defined as

\begin{align*} \mathcal{U}^R(P, \mathcal{Q}) & = \sup _{\eta \in \mathcal{E}^R_{\mathcal{Q}}} (\eta \circ P)[U] \: , \end{align*}

in which the expectation is the extended integral defined in Definition 1.4.

Lemma 2.17

Let \(\mathcal{Q}' \subseteq \mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then

\begin{align*} \mathcal{U}(P, \mathcal{Q}’) & \ge \mathcal{U}(P, \mathcal{Q}) \: . \end{align*}
Proof

The next lemma shows that the larger set of randomized E-variables does not allow to achieve a larger expected utility than the set of E-variables.

Let \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) be a utility function and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) a set of probability measures. Let \(P \in \mathcal{P}(\mathcal{X})\). Then

\begin{align*} \mathcal{U}^R(P, \mathcal{Q}) & = \mathcal{U}(P, \mathcal{Q}) \: . \end{align*}
Proof

Since \(\mathcal{E}_{\mathcal{Q}} \subseteq \mathcal{E}^R_{\mathcal{Q}}\), the left hand side is greater than or equal to the right hand side.

For the other direction, let \(\eta \in \mathcal{E}^R_{\mathcal{Q}}\) be a randomized E-variable. By concavity of \(U\),

\begin{align*} (\eta \circ P)[U] & = P[x \mapsto \eta (x)[U]] \le P[x \mapsto U(\eta (x)[\operatorname{id}])] \end{align*}

The function \(x \mapsto \eta (x)[\operatorname{id}]\) is a measurable function and belongs to \(\mathcal{E}_{\mathcal{Q}}\).

Suppose that there exists an event \(A\) such that \(P(A) {\gt} 0\) and \(Q(A) = 0\) for all \(Q \in \mathcal{Q}\). That is, we don’t have \(P \ll \mathcal{Q}\). Then

\begin{align*} \mathcal{U}(P, \mathcal{Q}) & = +\infty \: . \end{align*}
Proof

The function \(f : \mathcal{X} \to \mathbb {R}_{+,\infty }\) defined by \(f(x) = +\infty \) if \(x \in A\) and \(f(x) = 0\) otherwise is an E-variable for \(\mathcal{Q}\) since \(Q[f] = 0\) for all \(Q \in \mathcal{Q}\).

\begin{align*} \mathcal{U}(P, \mathcal{Q}) & \ge P[\log f] = P(A) \cdot (+\infty ) = +\infty \: . \end{align*}
Lemma 2.20

The function \(R \mapsto \mathcal{U}(R, \mathcal{P})\) is convex.

Proof

Let \(R_1, R_2 \in \mathcal{P}(\mathcal{X})\) and let \(\theta \in [0,1]\).

\begin{align*} \mathcal{U}(\theta R_1 + (1 - \theta ) R_2, \mathcal{P}) & = \sup _{f \in \mathcal{E}_{\mathcal{P}}} (\theta R_1 + (1 - \theta ) R_2)\left[\log f\right] \\ & = \sup _{f \in \mathcal{E}_{\mathcal{P}}} \left\{ \theta R_1[\log f] + (1 - \theta ) R_2[\log f]\right\} \\ & \le \theta \sup _{f \in \mathcal{E}_{\mathcal{P}}} R_1[\log f] + (1 - \theta ) \sup _{f \in \mathcal{E}_{\mathcal{P}}} R_2[\log f] \\ & = \theta \mathcal{U}(R_1, \mathcal{P}) + (1 - \theta ) \mathcal{U}(R_2, \mathcal{P}) \: . \end{align*}

2.3 Data-processing inequality for e-variables

Lemma 2.21
#

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and let \(\phi : \mathcal{X} \to \mathcal{Y}\) be a measurable function. Then for all \(g \in \mathcal{E}_{\phi _* \mathcal{Q}}\), \(g \circ \phi \in \mathcal{E}_{\mathcal{Q}}\).

Proof

For \(Q \in \mathcal{Q}\),

\begin{align*} Q[g \circ \phi ] & = (\phi _* Q)[g] \le 1 \: . \end{align*}
Lemma 2.22
#

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel. Then for all \(\xi \in \mathcal{E}_{\kappa \circ \mathcal{Q}}^R\), \(\xi \circ \kappa \in \mathcal{E}^R_{\mathcal{Q}}\).

Proof

For \(Q \in \mathcal{Q}\),

\begin{align*} ((\xi \circ \kappa ) \circ Q)[\operatorname{id}] & = (\xi \circ (\kappa \circ Q))[\operatorname{id}] \le 1 \: . \end{align*}
Theorem 2.23 Data processing inequality with randomized e-variables

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and \(P \in \mathcal{P}(\mathcal{X})\). Let \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) be a utility function. Then for all Markov kernels \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\),

\begin{align*} \mathcal{U}^R(P, \mathcal{Q}) & \ge \mathcal{U}^R(\kappa \circ P, \kappa \circ \mathcal{Q}) \: . \end{align*}
Proof

By Lemma 2.22, \(\{ \xi \circ \kappa \mid \xi \in \mathcal{E}^R_{\kappa \circ \mathcal{Q}}\} \subseteq \mathcal{E}^R_{\mathcal{Q}}\), hence

\begin{align*} \sup _{\eta \in \mathcal{E}^R_{\mathcal{Q}}} (\eta \circ P)[U] & \ge \sup _{\eta \in \{ \xi \circ \kappa \mid \xi \in \mathcal{E}^R_{\kappa \circ \mathcal{Q}}\} } (\eta \circ P)[U] = \sup _{\xi \in \mathcal{E}^R_{\kappa \circ \mathcal{Q}}} (\xi \circ \kappa \circ P)[U] \: . \end{align*}
Lemma 2.24 Data processing inequality with e-variables

Let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel, \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then

\begin{align*} \mathcal{U}(\kappa \circ P, \kappa \circ \mathcal{Q}) & \le \mathcal{U}(P, \mathcal{Q}) \: . \end{align*}
Proof

We can use Lemma 2.18 to obtain the data-processing with a Markov kernel for e-variables from the result for randomized e-variables (Theorem 2.23).

Lemma 2.25 Data processing inequality with e-variables

Let \(f : \mathcal{X} \to \mathcal{Y}\) be a measurable function, \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then

\begin{align*} \mathcal{U}(f_*P, f_* \mathcal{Q}) & \le \mathcal{U}(P, \mathcal{Q}) \: . \end{align*}
Proof

Use Lemma 2.24 with the deterministic Markov kernel given by \(f\).

Lemma 2.26

Let \(\phi : \mathcal{X} \to \mathcal{Y}\) be a measurable embedding, \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then

\begin{align*} \mathcal{U}(\phi _* P, \phi _* \mathcal{Q}) & = \mathcal{U}(P, \mathcal{Q}) \: . \end{align*}
Proof
Lemma 2.27

Let \(R \in \mathcal{P}(\mathcal{X})\), \(\mathcal{P} \subseteq \mathcal{P}(\mathcal{X})\) and let \(\phi : \mathcal{X} \to \mathcal{X}\) be a measurable map such that \(\phi \circ \phi = \operatorname{id}\). Then

\begin{align*} \mathcal{U}(R, \phi _* \mathcal{P}) & = \mathcal{U}(\phi _* R, \mathcal{P}) \: . \end{align*}
Proof

To prove the first inequality, we use Lemma 2.25:

\begin{align*} \mathcal{U}(R, \phi _* \mathcal{P}) & = \mathcal{U}(\phi _* (\phi _* R), \phi _* \mathcal{P}) \\ & \le \mathcal{U}(\phi _* R, \mathcal{P}) \: . \end{align*}

For the reverse inequality,

\begin{align*} \mathcal{U}(\phi _* R, \mathcal{P}) & = \mathcal{U}(\phi _* R, \phi _* (\phi _* \mathcal{P})) \\ & \le \mathcal{U}(R, \phi _* \mathcal{P}) \: . \end{align*}

2.4 Numeraire and duality

This section gathers results from [ LRR24 ] .

Definition 2.28
#

Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). A numeraire for \((P, \mathcal{Q})\) is a \(P\)-almost surely positive e-variable \(f^* \in \mathcal{E}_{\mathcal{Q}}\) such that \(P[f / f^*] \le 1\) for all \(f \in \mathcal{E}_{\mathcal{Q}}\) .

Lemma 2.29

Let \(f^*\) be a numeraire for \((P, \mathcal{Q})\) and let \(f \in \mathcal{E}_{\mathcal{Q}}\). Then \(P\left[\log \frac{f}{f^*}\right] \le 0\).

Proof
Lemma 2.30

Let \(f\) be a \(P\)-almost surely positive E-variable for a set of probability measures \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then \(f\) is a numeraire for \((P, \mathcal{Q})\) if and only if \(P[\log \frac{g}{f}] \le 0\) for all \(g \in \mathcal{E}_{\mathcal{Q}}\).

Proof
Lemma 2.31

Let \(f^*\) be a numeraire for \((P, \mathcal{Q})\). Then for all \(f \in \mathcal{E}_{\mathcal{Q}}\),

\begin{align*} P[\log f] & \le P[\log f^*] \: . \end{align*}
Proof

Let \(f^*\) be a numeraire for \((P, \mathcal{Q})\). Then \(P[\log f^*] \ge 0\).

Proof

Use Lemma 2.31 with the e-variable \(f = 1\).

Lemma 2.33

Let \(\mathcal{Q}_1 \subseteq \mathcal{Q}_2\). If \(f^*\) is a numeraire for \((P, \mathcal{Q}_1)\) and \(f^* \in \mathcal{E}_{\mathcal{Q}_2}\) then \(f^*\) is also a numeraire for \((P, \mathcal{Q}_2)\).

Proof

For \(f \in \mathcal{E}_{\mathcal{Q}_2}\), we have \(f \in \mathcal{E}_{\mathcal{Q}_1}\) since \(\mathcal{Q}_1 \subseteq \mathcal{Q}_2\). Then \(P\left[f/f^*\right] \le 1\) by the numeraire property for \((P, \mathcal{Q}_1)\). Thus \(f^*\) is a numeraire for \((P, \mathcal{Q}_2)\).

Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). If \(f^*\) is a numeraire for \((P, \mathcal{Q})\) then

\begin{align*} \mathcal{U}(P, \mathcal{Q}) & = P[U(f^*)] \: . \end{align*}
Proof

2.4.1 Existence of a numeraire

Lemma 2.35

Let \((X_n)_{n \in \mathbb {N}}\) be a sequence of measurable functions from \(\mathcal{X}\) to \(\mathbb {R}_{+,\infty }\) and let \(Q \in \mathcal{P}(\mathcal{X})\). There exists a sequence of measurable functions \(\tilde{X}_n \in \mathrm{conv}(X_n, X_{n+1}, \ldots )\) and \(X : \mathcal{X} \to \mathbb {R}_{+, \infty }\) such that \(\tilde{X}_n \to X\) \(Q\)-almost surely.

Proof

Proved in the Brownian motion project.

Lemma 2.36

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) a concave function which is bounded from above. Then there exists \(f^* \in \mathcal{E}_{\mathcal{Q}}\) such that

\begin{align*} \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[U(f)] & = P[U(f^*)] \: . \end{align*}

The properties of \(\mathcal{E}_{\mathcal{Q}}\) we use for the proof of this lemma are convexity and the fact that if \(f_n \in \mathcal{E}_{\mathcal{Q}}\) then \(\liminf _{n \to \infty } f_n \in \mathcal{E}_{\mathcal{Q}}\).

Proof

Let \((f_n)_{n \in \mathbb {N}}\) be a sequence of e-variables in \(\mathcal{E}_{\mathcal{Q}}\) such that \(n \mapsto P[U(f_n)]\) is non-decreasing and \(P[U(f_n)] \to \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[U(f)]\). Let \(\tilde{f}_n\) be a sequence of measurable functions and let \(\tilde{f} : \mathcal{X} \to \mathbb {R}_{+, \infty }\), with \(\tilde{f}_n \in \mathrm{conv}(f_n, f_{n+1}, \ldots )\) such that \(\tilde{f}_n \to \tilde{f}\) \(P\)-almost surely. That sequence exists by Lemma 2.35. By convexity of \(\mathcal{E}_{\mathcal{Q}}\), \(\tilde{f}_n \in \mathcal{E}_{\mathcal{Q}}\) for all \(n\).

Let \(f^* = \liminf _{n \to \infty } \tilde{f}_n\). Note that \(f^*\) is a measurable function such that \(f^* = \tilde{f}\) \(P\)-almost surely. We have \(f^* \in \mathcal{E}_{\mathcal{Q}}\) by an application of Fatou’s lemma: for \(Q \in \mathcal{Q}\),

\begin{align*} Q[f^*] = Q[\liminf _{n \to \infty } \tilde{f}_n] \le \liminf _{n \to \infty } Q[\tilde{f}_n] \le 1 \: . \end{align*}

By concavity of \(U\) and the fact that \(P[U(f_n)]\) is non-decreasing, we have

\begin{align*} P[U(\tilde{f}_n)] & = P[U(\sum _{i=n}^{m_n} \lambda _{n,i} f_{i})] \ge \sum _{i=n}^{m_n} \lambda _{n,i} P[U(f_{i})] \ge P[U(f_n)] \: . \end{align*}

We also have \(f^* = \limsup _{n \to \infty } \tilde{f}_n\) \(P\)-almost surely, hence by Fatou’s lemma (using that \(U\) is bounded from above),

\begin{align*} P[U(f^*)] & = P[U(\limsup _{n \to \infty } \tilde{f}_n)] \ge \limsup _{n \to \infty } P[U(\tilde{f}_n)] \ge \limsup _{n \to \infty } P[U(f_n)] = \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[U(f)] \: . \end{align*}

Since \(f^* \in \mathcal{E}_{\mathcal{Q}}\), we also have \(P[U(f^*)] \le \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[U(f)]\), which proves the equality.

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) a concave function which is bounded from above. Then there exists \(f^* \in \mathcal{E}_{\mathcal{Q}}\) such that

\begin{align*} \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[U(f)] & = P[U(f^*)] \: , \end{align*}

and such that all e-variables are \(P\)-a.s. finite on \(\{ f^* {\lt} \infty \} \).

Proof

Let \(f_0\) be as in Lemma 2.36. The set \(\{ f_0 = \infty \} \) is \(\mathcal{Q}\)-null by Lemma 2.12.

TODO

Lemma 2.38

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) a non-decreasing concave \(C^1\) function which is bounded from above. Then the e-variable \(f^*\) in Lemma 2.36 is such that for all e-variables \(g \in \mathcal{E}_{\mathcal{Q}}\),

\begin{align*} P\left[ U’(f^*) (g - f^*) \right] \le 0 \: . \end{align*}
Proof

For \(f \in \mathcal{E}_{\mathcal{Q}}\), let \(f_t = t f + (1-t) f^*\) for \(t \in [0,1]\). By concavity of \(U\), \(U(f_t) \ge t U(f) + (1-t) U(f^*)\), hence

\begin{align*} \frac{U(f_t) - U(f^*)}{t} & \ge U(f) - U(f^*) \: . \end{align*}

TODO

Theorem 2.39 Existence of the numeraire

Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). A numeraire for \((P, \mathcal{Q})\) exists and is \(P\)-a.e. unique.

Proof

We will write \(E^*_{P, \mathcal{Q}}\) for the numeraire for \((P, \mathcal{Q})\).

2.4.2 Products

Theorem 2.40 Numeraire of a product

Let \(P_1 \in \mathcal{P}(\mathcal{X}), P_2 \in \mathcal{P}(\mathcal{Y})\) and \(\mathcal{Q}_1 \subseteq \mathcal{P}(\mathcal{X}), \mathcal{Q}_2 \subseteq \mathcal{P}(\mathcal{Y})\). Then the numeraire for the product \((P_1 \times P_2, \mathcal{Q}_1 \times \mathcal{Q}_2)\) is given by the product of the numeraires for \((P_1, \mathcal{Q}_1)\) and \((P_2, \mathcal{Q}_2)\):

\begin{align*} E^*_{P_1 \times P_2, \mathcal{Q}_1 \times \mathcal{Q}_2} & = E^*_{P_1, \mathcal{Q}_1} E^*_{P_2, \mathcal{Q}_2} \: . \end{align*}

Its logarithmic utility is given by

\begin{align*} (P_1 \times P_2)[\log E^*_{P_1 \times P_2, \mathcal{Q}_1 \times \mathcal{Q}_2}] & = P_1[\log E^*_{P_1, \mathcal{Q}_1}] + P_2[\log E^*_{P_2, \mathcal{Q}_2}] \: . \end{align*}
Proof

We first need to check that \(E^*_{P_1, \mathcal{Q}_1} E^*_{P_2, \mathcal{Q}_2}\) is in \(\mathcal{E}_{\mathcal{Q}_1 \times \mathcal{Q}_2}\). For \(Q_1 \in \mathcal{Q}_1\) and \(Q_2 \in \mathcal{Q}_2\), we have

\begin{align*} (Q_1 \times Q_2)\left[E^*_{P_1, \mathcal{Q}_1} E^*_{P_2, \mathcal{Q}_2}\right] & = Q_1[E^*_{P_1, \mathcal{Q}_1}] Q_2[E^*_{P_2, \mathcal{Q}_2}] \le 1 \: . \end{align*}

Let’s now check the numeraire property. Let \(f \in \mathcal{E}_{\mathcal{Q}_1 \times \mathcal{Q}_2}\). Let \(E^*_1 = E^*_{P_1, \mathcal{Q}_1}\) and \(E^*_2 = E^*_{P_2, \mathcal{Q}_2}\).

\begin{align*} (P_1 \times P_2)\left[\frac{f}{E^*_1 E^*_2} \right] & = P_1\left[\frac{1}{E_1^*}P_2\left[\frac{f}{E^*_2}\right] \right] \: . \end{align*}

If we can show that \(x \mapsto P_2\left[\frac{f(x, \cdot )}{E^*_2}\right]\) is in \(\mathcal{E}_{\mathcal{Q}_1}\), then we can apply the definition of the numeraire for \((P_1, \mathcal{Q}_1)\) to obtain

\begin{align*} (P_1 \times P_2)\left[\frac{f}{E^*_1 E^*_2} \right] & = P_1\left[\frac{1}{E_1^*}P_2\left[\frac{f}{E^*_2}\right] \right] \le 1 \: . \end{align*}

Let then \(Q_1 \in \mathcal{Q}_1\). We want to show that \(Q_1 \left[P_2\left[\frac{f(x, \cdot )}{E^*_2}\right]\right] \le 1\). By Fubini’s theorem, we have

\begin{align*} Q_1 \left[P_2\left[\frac{f(x, \cdot )}{E^*_2}\right]\right] & = P_2\left[\frac{Q_1 \left[f(\cdot , y)\right]}{E^*_2}\right] \: . \end{align*}

It now suffices to show that \(y \mapsto Q_1 \left[f(\cdot , y)\right]\) is in \(\mathcal{E}_{\mathcal{Q}_2}\). Let \(Q_2 \in \mathcal{Q}_2\). Then

\begin{align*} Q_2\left[y \mapsto Q_1 \left[f(\cdot , y)\right]\right] = (Q_1 \times Q_2)\left[f\right] \le 1 \: . \end{align*}

The last inequality holds since \(f \in \mathcal{E}_{\mathcal{Q}_1 \times \mathcal{Q}_2}\).

Lemma 2.41

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) and \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) be a set of Markov kernels. If \(f \in \mathcal{E}_{\mathcal{Q}\otimes H}\) then for all \(\eta \in H\), the function \(x \mapsto \eta (x)[f(x, \cdot )]\) is in \(\mathcal{E}_{\mathcal{Q}}\).

Proof

For \(Q \in \mathcal{Q}\) and \(f \in \mathcal{E}_{\mathcal{Q}\otimes H}\), \(Q[x \mapsto \eta (x)[f]] = (Q \otimes \eta )[f] \le 1\), hence \((x \mapsto \eta (x)[f(x, \cdot )]) \in \mathcal{E}_{\mathcal{Q}}\).

Lemma 2.42

\(E^*_{P \otimes \kappa , \mathcal{Q} \otimes \kappa } =_{(P\otimes \kappa ) a.e.} (x, y) \mapsto E^*_{P, \mathcal{Q}}(x)\) for any Markov kernel \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\).

Proof

First we need to show that \(E^*_{P, \mathcal{Q}}\) is in \(\mathcal{E}_{\mathcal{Q} \otimes \kappa }\). It is measurable and for \(Q \in \mathcal{Q}\),

\begin{align*} (Q \otimes \kappa )\left[E^*_{P, \mathcal{Q}}\right] = Q\left[E^*_{P, \mathcal{Q}}\right] \le 1 \: . \end{align*}

We then check the numeraire condition. By Lemma 2.41, \((x \mapsto \kappa (x)[f]) \in \mathcal{E}_{\mathcal{Q}}\). By the definition of the numeraire, we then have

\begin{align*} P\left[\frac{\kappa (x)[f]}{E^*_{P, \mathcal{Q}}}\right] \le 1 \: . \end{align*}

That is, \((P \otimes \kappa )\left[\frac{f}{E^*_{P, \mathcal{Q}}}\right] \le 1\) for all \(f \in \mathcal{E}_{\mathcal{Q}\otimes \kappa }\). We have proved that \(E^*_{P, \mathcal{Q}}\) is the numeraire for \((P \otimes \kappa , \mathcal{Q} \otimes \kappa )\).

Theorem 2.43

For any \(f \in \mathcal{E}_{\mathcal{Q} \otimes H}\), we have

\begin{align*} (P \otimes \kappa )\left[\frac{f}{E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , \{ P\} \otimes H}}\right] & \le 1 \: . \end{align*}

Note that this proves the numeraire property for the candidate \(E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , P \otimes H}\), but we don’t know if it is an e-variable, so can’t conclude that it’s the numeraire. As a consequence,

\begin{align*} (P \otimes \kappa )\left[\log E^*_{P \otimes \kappa , \mathcal{Q} \otimes H}\right] \le P\left[\log E^*_{P, \mathcal{Q}}\right] + (P \otimes \kappa )\left[\log E^*_{P\otimes \kappa , \{ P\} \otimes H}\right] \: . \end{align*}

If \(E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , \{ P\} \otimes H}\) was an e-variable, then we would have equality.

Proof

Let \(f \in \mathcal{E}_{\mathcal{Q} \otimes H}\).

\begin{align*} (P \otimes \kappa )\left[\frac{f}{E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , P \otimes H}}\right] & = (P \otimes \kappa )\left[\frac{f/E^*_{P, \mathcal{Q}}}{E^*_{P \otimes \kappa , P \otimes H}}\right] \: . \end{align*}

It suffices to show that \(f/E^*_{P, \mathcal{Q}}\) is in \(\mathcal{E}_{P \otimes H}\) to obtain that this is less than 1 by the numeraire property. Let \(\eta \in H\). By Lemma 2.42,

\begin{align*} (P \otimes \eta ) \left[\frac{f}{E^*_{P, \mathcal{Q}}}\right] & = (P \otimes \eta ) \left[\frac{f}{E^*_{P \otimes \eta , \mathcal{Q} \otimes \eta }}\right] \end{align*}

and since \(f \in \mathcal{E}_{\mathcal{Q} \otimes H}\) it is in \(\mathcal{E}_{\mathcal{Q} \otimes \eta }\) and the numeraire property gives that this ratio is less than or equal to 1. We conclude that \(E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , P \otimes H}\) satisfies the numeraire property.

We then have

\begin{align*} (P \otimes \kappa )\left[\log \frac{f}{E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , P \otimes H}}\right] & \le \log \left( (P \otimes \kappa )\left[\frac{f}{E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , P \otimes H}}\right] \right) \le 0 \: . \end{align*}

We conclude that

\begin{align*} P[\log f] \le P\left[\log E^*_{P, \mathcal{Q}}\right] + (P \otimes \kappa )\left[\log E^*_{P \otimes \kappa , P \otimes H}\right] \: . \end{align*}

Taking the supremum over \(f \in \mathcal{E}_{\mathcal{Q} \otimes H}\), we obtain the desired inequality.

Definition 2.44

A jointly measurable function \(f : \mathcal{X} \times \mathcal{Y} \to \mathbb {R}_{+, \infty }\) is a conditional e-variable for a set of Markov kernels \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) if \(f(x, \cdot )\) is in \(\mathcal{E}_{H(x)}\) for all \(x \in \mathcal{X}\) (in which \(H(x) = \{ \eta (x) \mid \eta \in H\} \)). We denote the set of conditional e-variables for \(H\) by \(\mathcal{E}_H\).

Definition 2.45

Let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel and \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) a set of Markov kernels. A conditional e-variable \(f\) for \(H\) is a conditional numeraire for \((\kappa , H)\) if for all \(x \in \mathcal{X}\), the function \(f(x, \cdot )\) is a numeraire for \((\kappa (x), H(x))\). We denote the conditional numeraire for \((\kappa , H)\) by \(E^*_{\kappa , H}\) (if it exists).

As we will see, conditional numeraires are not always guaranteed to exist.

Question: is there always a \(P\)-a.e. conditional numeraire? That is, a jointly measurable function \(f\) such that \(f(x, \cdot )\) is a numeraire for \((\kappa (x), H(x))\) for only \(P\)-almost all \(x \in \mathcal{X}\) instead of all \(x \in \mathcal{X}\)?

Lemma 2.46

If there exists a conditional numeraire for \((\kappa , \{ \eta \} )\), then \(1/E^*_{\kappa , \{ \eta \} }\) is a Radon-Nikodym derivative for \(\eta \) with respect to \(\kappa \).

Proof

\(1/E^*_{\kappa , \{ \eta \} }\) is jointly measurable and satisfies for all \(x \in \mathcal{X}\), \(1/E^*_{\kappa , \{ \eta \} }(x, \cdot ) =_{a.e.} \frac{d\eta (x)}{d \kappa (x)}\) since that’s the numeraire for a singleton. That’s the definition of a Radon-Nikodym derivative of kernels.

The last lemma implies that \(E^*_{\kappa , H}\) will not always exist. Indeed, Radon-Nikodym derivatives of kernels do not always exist, although weak hypotheses on \(\mathcal{X}\) or \(\mathcal{Y}\) can ensure that they do. \(\mathcal{X}\) being countable or \(\mathcal{Y}\) having countably generated sigma-algebra (for example \(\mathcal{Y}\) standard Borel) is enough.

Theorem 2.47

If a conditional numeraire exists for \((\kappa , H)\), then \(E^*_{P \otimes \kappa , \mathcal{Q} \otimes H} =_{a.e.} E^*_{P, \mathcal{Q}}E^*_{\kappa , H}\).

Proof

\(E^*_{\kappa , H}\) is a numeraire for \((P \otimes \kappa , \{ P\} \otimes H)\), so we get the numeraire property as in Theorem 2.43. We now prove that \(E^*_{P, \mathcal{Q}}E^*_{\kappa , H} \in \mathcal{E}_{\mathcal{Q} \otimes H}\). Let \(Q \in \mathcal{Q}\) and \(\eta \in H\).

\begin{align*} (Q \otimes \eta )\left[E^*_{P, \mathcal{Q}}E^*_{\kappa , H}\right] & = Q\left[x \mapsto E^*_{P, \mathcal{Q}}(x) \eta (x)\left[E^*_{\kappa (x), H(x)}\right] \right] \: . \end{align*}

For all \(x\), \(\eta (x)\left[E^*_{\kappa (x), H(x)}\right] \le 1\), since \(E^*_{\kappa (x), H(x)}\) is an e-variable for \(H(x)\).

\begin{align*} (Q \otimes \eta )\left[E^*_{P, \mathcal{Q}}E^*_{\kappa , H}\right] & \le Q\left[x \mapsto E^*_{P, \mathcal{Q}}(x)\right] \le 1 \: . \end{align*}
Lemma 2.48

Let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel and \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) a set of Markov kernels. If \(\mathcal{X}\) is countable with measurable singletons then there exists a conditional numeraire for \((\kappa , H)\).

Proof

For each \(x \in \mathcal{X}\), we can apply Theorem 2.39 to the Markov kernel \(\kappa (x)\) and the set of kernels \(H(x)\) to obtain a numeraire \(E^*_{\kappa (x), H(x)}\). If \(\mathcal{X}\) is countable, we can simply take the function \(E^*_{\kappa , H}(x,y) = E^*_{\kappa (x), H(x)}(y)\), and the countability ensures the joint measurability.

Theorem 2.49

Let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel and \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) a set of Markov kernels. If either \(\mathcal{X}\) is countable or \(\mathcal{Y}\) is countably generated, then there exists a conditional numeraire for \((\kappa , H)\).

Proof

The case of countable \(\mathcal{X}\) is covered by Lemma 2.48. (TODO: only for measurable singletons though)

TODO: \(\kappa \ll H\) hyp?

Let’s now consider the case where \(\mathcal{Y}\) is countably generated. Then there exists a sequence of finer and finer partitions of \(\mathcal{Y}\) into finitely many measurable sets \((A_{n,m})_{n \in \mathbb {N}, m \le 2^n}\) with \(\mathcal{Y} = \bigcup _{m \le 2^n} A_{n,m}\) for all \(n\), and such that the sigma-algebra on \(\mathcal{Y}\) is the \(\sigma \)-algebra generated by the sets \(A_{n,m}\). Let \(\mathcal{F}\) be the filtration generated by the successive partitions, that is, \(\mathcal{F}_n = \sigma (\{ A_{n,m} \mid m \le 2^n\} )\).

TODO

2.4.3 Reverse information projection and duality.

Definition 2.50

Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). The reverse information projection (RIPr) of \(P\) onto \(\mathcal{Q}\) is the finite measure \(P^*_{\mathcal{Q}}\) absolutely continuous with respect to \(P\) such that

\begin{align*} \frac{dP^*_{\mathcal{Q}}}{dP} = \frac{1}{E^*_{P, \mathcal{Q}}} \: . \end{align*}

It satisfies \(P^*_{\mathcal{Q}}[\mathcal{X}] \le 1\) and the inequality can be strict.

Definition 2.51

For a set of probability measures \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\), the effective set of measures \(\mathcal{Q}_{\operatorname{eff}}\) is the set \(\{ \mu \in \mathcal{M}_+(\mathcal{X}) \mid \forall f \in \mathcal{E}_{\mathcal{Q}}, \: \mu [f] \le 1\} \).

We could also define the effective set of measures for randomized E-variables, but it actually gives the same set.

Lemma 2.52

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures. The effective set \(\mathcal{Q}_{\operatorname{eff}}\) is convex and satisfies \(0 \in \mathcal{Q}_{\operatorname{eff}}\) and \(\mathcal{Q} \subseteq \mathcal{Q}_{\operatorname{eff}}\).

Proof
Lemma 2.53

Let \(Q \in \mathcal{P}(\mathcal{X})\) be a probability measure. Then \(\{ Q\} _{\operatorname{eff}} = \{ Q' \in \mathcal{M}(\mathcal{X}) \mid Q' \le Q\} \), in which \(Q' \le Q\) means that \(Q'(A) \le Q(A)\) for all measurable sets \(A \subseteq \mathcal{X}\).

Proof
\begin{align*} \{ Q\} _{\operatorname{eff}} & = \{ Q’ \in \mathcal{M}(\mathcal{X}) \mid \forall f, \ (\forall Q” \in \{ Q\} , Q”[f] \le 1) \implies Q’[f] \le 1\} \\ & = \{ Q’ \in \mathcal{M}(\mathcal{X}) \mid \forall f, \ Q[f] \le 1 \implies Q’[f] \le 1\} \end{align*}

This is the set of finite measures \(\{ Q' \mid Q' \le Q\} \). Indeed, if \(Q' \le Q\), then for all \(f\) with \(Q[f] \le 1\), \(Q'[f] \le Q[f] \le 1\). Conversely, if \(Q'\) is such that \((\forall f, \ Q[f] \le 1 \implies Q'[f] \le 1)\), then for any measurable set \(A\), we can take the measurable function \(f = Q(A)^{-1} \cdot \mathbb {I}_A\) (in which \(0^{-1} = \infty \)) to obtain \(Q'(A) \le Q(A)\).

Lemma 2.54

Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) and let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel. Then \(\kappa \circ \mathcal{Q}_{\operatorname{eff}} \subseteq (\kappa \circ \mathcal{Q})_{\operatorname{eff}}\).

Proof
\begin{align*} \kappa \circ \mathcal{Q}_{\operatorname{eff}} & = \{ \kappa \circ Q \mid \forall f, (\forall Q’ \in \mathcal{Q}, Q’[f] \le 1) \implies Q[f] \le 1\} \\ (\kappa \circ \mathcal{Q})_{\operatorname{eff}} & = \{ R \mid \forall f, (\forall Q’ \in \mathcal{Q}, (\kappa \circ Q’)[f] \le 1) \implies R[f] \le 1\} \end{align*}

Let \(\kappa \circ Q \in \kappa \circ \mathcal{Q}_{\operatorname{eff}}\) and \(f\) such that \(\forall Q' \in \mathcal{Q}, (\kappa \circ Q')[f] \le 1\). That is, \(\forall Q' \in \mathcal{Q}, Q'[x \mapsto \kappa (x)[f]] \le 1\). Then since \(Q \in \mathcal{Q}_{\operatorname{eff}}\), we have \(Q[x \mapsto \kappa (x)[f]] \le 1\). We have proved that \(\kappa \circ Q \in (\kappa \circ \mathcal{Q})_{\operatorname{eff}}\).

Lemma 2.55

Let \(\mathcal{Q}_1 \subseteq \mathcal{Q}_2 \subseteq \mathcal{P}(\mathcal{X})\) be two sets of probability measures. Then \(\mathcal{Q}_{1, \operatorname{eff}} \subseteq \mathcal{Q}_{2, \operatorname{eff}}\).

Proof

Since \(\mathcal{Q}_1 \subseteq \mathcal{Q}_2\) we have \(\mathcal{E}_{\mathcal{Q}_2} \subseteq \mathcal{E}_{\mathcal{Q}_1}\) (Lemma 2.2). Then

\begin{align*} \mathcal{Q}_{1, \operatorname{eff}} & = \{ \mu \in \mathcal{M}_+(\mathcal{X}) \mid \forall f \in \mathcal{E}_{\mathcal{Q}_1}, \: \mu [f] \le 1\} \\ & \subseteq \{ \mu \in \mathcal{M}_+(\mathcal{X}) \mid \forall f \in \mathcal{E}_{\mathcal{Q}_2}, \: \mu [f] \le 1\} \\ & = \mathcal{Q}_{2, \operatorname{eff}} \: . \end{align*}

Let \(\operatorname{KL}(P, Q)\) be the Kullback-Leibler divergence from \(P\) to \(Q\), defined for finite measures as

\begin{align*} \operatorname{KL}(P, Q) & = \begin{cases} P[\log \frac{dP}{dQ}] & \text{if } P \ll Q \\ +\infty & \text{otherwise} \end{cases} \: . \end{align*}

Note that we don’t correct the divergence if \(P\) and \(Q\) are not probability measures.

Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). If \(P \ll \mathcal{Q}\) then

\begin{align*} P[\log E^*_{P, \mathcal{Q}}] = \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[\log f] = \inf _{\mu \in \mathcal{Q}_{\operatorname{eff}}} \operatorname{KL}(P, \mu ) = \operatorname{KL}(P, P^*_{\mathcal{Q}}) \: . \end{align*}
Proof

Let \(P, Q \in \mathcal{P}(\mathcal{X})\) be two probability measures with \(P \ll Q\). Then

\begin{align*} \sup _{f \in \mathcal{E}_{\{ Q\} }} P[\log f] = \operatorname{KL}(P, Q) \: . \end{align*}
Proof

Since \(\operatorname{KL}\) is non-increasing in the second argument,

\begin{align*} \inf _{Q' \le Q} \operatorname{KL}(P, Q’) = \operatorname{KL}(P, Q) \: . \end{align*}

Since \(\{ Q\} _{\operatorname{eff}} = \{ Q' \in \mathcal{M}(\mathcal{X}) \mid Q' \le Q\} \) (Lemma 2.53), we obtained \(\operatorname{KL}(P, Q) = \inf _{Q' \in \{ Q\} _{\operatorname{eff}}} \operatorname{KL}(P, Q')\). By the duality of numeraire and \(\operatorname{KL}\) divergence (Theorem 2.56), we have

\begin{align*} \sup _{f \in \mathcal{E}_{\{ Q\} }} P[\log f] = \inf _{Q' \in \{ Q\} _{\operatorname{eff}}} \operatorname{KL}(P, Q’) = \operatorname{KL}(P, Q) \: . \end{align*}

KL data processing from e-variables

The data processing inequality for the numeraire utility implies the data processing inequality for KL divergences.

Let \(P, Q \in \mathcal{P}(\mathcal{X})\). Then for all Markov kernels \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\),

\begin{align*} \operatorname{KL}(P, Q) & \ge \operatorname{KL}(\kappa \circ P, \kappa \circ Q) \: . \end{align*}
Proof

By Lemma 2.57, \(\operatorname{KL}(P, Q) = \sup _{f \in \mathcal{E}_{\{ Q\} }} P[\log f]\). Then by Lemma 2.24, we have

\begin{align*} \sup _{f \in \mathcal{E}_{\{ Q\} }} P[\log f] & \ge \sup _{g \in \mathcal{E}_{\{ \kappa \circ Q\} }} (\kappa \circ P)[\log g] \: . \end{align*}

And that last term is \(\operatorname{KL}(\kappa \circ P, \kappa \circ Q)\), again by Lemma 2.57.