Weak and TV consistency in Bayesian uncertainty quantification using disintegration


Using standard techniques in Probability theory we prove a series of results relevant in the theory of Bayesian uncertainty quantification (UQ). Using the approach, found in the Bayesian literature, of defining the posterior distribution through a disintegration argument, and using weak and total variation convergence, we are able to prove the existence and numerical consistency of the posterior measure in general functional (Banach) spaces. Relaying commonly on simpler proofs and weaker assumptions, we establish these basic results useful for the theoretical foundation of most common and current UQ problems.

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We thank Tan Bui-Thanh (UT Austin) for prompting us to work on this generalization and for several comments on a previous draft of the paper. Also to Peter Müller (UT Austin) and Fernanda Méndez (CIMAT) for invaluable comments during the many previous drafts of the paper. This research is partially founded by CONACYT CB-2016-01-284451, RDECOMM and ONRG Grants.

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A Auxiliary lemmas

A Auxiliary lemmas

Lemma 3

Let \(b_n, b: \Theta \rightarrow \mathbb {R}^+\) be bounded, \(\pi \)-integrable functions and let \(z_n = \int b_n(\theta ) \pi (\mathrm{{d}}\theta )\) and \(z = \int b(\theta ) \pi (\mathrm{{d}}\theta )\) and assume that \(| b_n(\theta ) - b(\theta ) | < k n^{-p}\) for all \(\theta \in \Theta , n > N\); \(k>0, p>1\) fixed. Then \(z_n \rightarrow z\) and \(\frac{b_n(\theta )}{z_n} \rightarrow \frac{b(\theta )}{z}\) with convergence rates

$$\begin{aligned} |z_n - z|< k n^{-p} ~~\text {and}~~ \left| \frac{b_n(\theta )}{z_n} - \frac{b(\theta )}{z} \right| < \frac{b(\theta )}{z} \frac{k}{z} n^{-p} + \frac{k}{z} n^{-p}, \end{aligned}$$

for all \(\theta \in \Theta \) and big enough n.


Since \(b_n(\theta ) \le l(\theta )=M\) then by dominated convergence \(z_n \rightarrow z\) (since \(M = \int l(\theta ) \pi \)). Since \(z_n, z >0\), \(b_n \rightarrow b\) already implies \(b_n/z_n \rightarrow b/z\).

Now, for the rate of convergence we have

$$\begin{aligned} |z_n - z| = \left| \int b_n(\theta ) \pi (\mathrm{{d}}\theta ) - \int b(\theta ) \pi (\mathrm{{d}}\theta ) \right|< \int \left| b_n(\theta ) - b(\theta ) \right| \pi (\mathrm{{d}}\theta ) < k n^{-p}, \end{aligned}$$

since \(\int \pi (\mathrm{{d}}\theta ) = 1\) and therefore \(\frac{| z_n - z|}{z} < \frac{k}{z} n^{-p}\).

Note that the first order Taylor series with residual of \(x^{-1}\) around \(x_0\) is \(x^{-1} = x_0^{-1} - x_0^{-2}(x - x_0) + x_1^{-3} (x - x_0)^2\), for \(x_1\) between x and \(x_0\). Assuming \(x,x_0> 0\) then

$$\begin{aligned} \frac{|x^{-1} - x_0^{-1}|}{x_0^{-1}} \le \frac{|x - x_0|}{x_0} + \left( \frac{x_0}{x_1} \right) ^{3} \left( \frac{x - x_0}{x_0} \right) ^2 . \end{aligned}$$

Let \(\frac{|x - x_0|}{x_0} < \epsilon \), then also \(\frac{|x - x_0|}{x_0} < \epsilon \) and \((1- \epsilon )< \frac{x_1}{x_0} < ( 1 + \epsilon )\). Since \(x^{-3}\) is decreasing then \(( 1 + \epsilon )^{-3}< \left( \frac{x_0}{x_1} \right) ^{3} < (1- \epsilon )^{-3}\). Therefore \(\left( \frac{x_0}{x_1} \right) ^{3} \left( \frac{x - x_0}{x_0} \right) ^2 < (1- \epsilon )^{-3} \epsilon ^2\). If the relative error \(\epsilon \) (of estimating \(x_0\) with x) is below 20%, \((1- \epsilon )^{-3} \epsilon ^2\) is already one order of magnitude smaller than \(\epsilon \). Then ignoring this last term

$$\begin{aligned} \frac{|x^{-1} - x_0^{-1}|}{x_0^{-1}} \lesssim \frac{|x - x_0|}{x_0} < \epsilon . \end{aligned}$$

Assume n is big enough such that the relative error \(\frac{| z_n - z|}{z} < \frac{k}{z} n^{-p}\) is small enough and we have \(\frac{| z_n^{-1} - z^{-1} |}{z^{-1}} \lesssim \frac{| z_n - z|}{z} < \frac{k}{z} n^{-p}\). Therefore

$$\begin{aligned} z^{-1} - \frac{k}{z^2} n^{-p}< z_n^{-1} < z^{-1} + \frac{k}{z^2} n^{-p}. \end{aligned}$$

Since \(b(\theta ) - k n^{-p}< b_n(\theta ) < b(\theta ) + k n^{-p}\), and given that we may assume \(0 < z^{-1}(1 - \frac{k}{z} n^{-p})\), then multiplying (15) with the former term we have

$$\begin{aligned}&(b(\theta ) - k n^{-p}) (z^{-1} - z^{-2} k n^{-p})< \frac{b_n(\theta )}{z_n}< (z^{-1} + z^{-2} k n^{-p}) (b(\theta ) + k n^{-p}) ,\\&\quad b(\theta )z^{-1} - b(\theta ) z^{-2} k n^{-p} - k n^{-p}z^{-1} + z^{-2} k^2 n^{-2p}< \frac{b_n(\theta )}{z_n} < \\&\quad b(\theta )z^{-1} + b(\theta ) z^{-2} k n^{-p} + k n^{-p}z^{-1} + z^{-2} k^2 n^{-2p} . \end{aligned}$$

Ignoring the two terms of 2p order, we obtain the result.

Lemma 4

With the setting of Lemma 3, let \(h(\theta )\) measurable and \(\hat{h}_n = \int h(\theta ) \frac{b_n(\theta )}{z_n} \pi (\mathrm{{d}}\theta )\) and \(\hat{h} = \int h(\theta ) \frac{b(\theta )}{z} \pi (\mathrm{{d}}\theta )\) exists. Then

$$\begin{aligned} | \hat{h}_n - \hat{h} | < E_1[| h |] \frac{k}{z} n^{-p} + E_0[| h |] \frac{k}{ z} n^{-p} \end{aligned}$$

where \(E_1[| h |] = \int | h(\theta ) | \frac{b(\theta )}{z} \pi (\mathrm{{d}}\theta )\) and \(E_0[| h |] = \int | h(\theta ) | \pi (\mathrm{{d}}\theta )\). Moreover, for all h non-negative and bounded, \(\int h(\theta ) \frac{b_n(\theta )}{z_n} \pi (\mathrm{{d}}\theta )\) and \(\int h(\theta ) \frac{b(\theta )}{z} \pi (\mathrm{{d}}\theta )\) implicitly define the probability measures \(p_n\) and p, then

$$\begin{aligned} ||p_n - p||_{TV} < \frac{k}{z} n^{-p} . \end{aligned}$$


We have

$$\begin{aligned} | \hat{h}_n - \hat{h} | \le \int |h(\theta )| \left| \frac{b_n(\theta )}{z_n} - \frac{b(\theta )}{z} \right| \pi (\mathrm{{d}}\theta ) \end{aligned}$$

and using Lemma 3 we obtain the first result. Moreover, if \(|h| \le 1\) then \(\hat{h} \frac{k}{z} n^{-p} + \hat{h}_0 \frac{k}{z} n^{-p} < 2\frac{k}{z} n^{-p}\) and, therefore,

$$\begin{aligned} \frac{1}{2} \max _{|h| \le 1} \left| \int h(\theta ) p_n(\mathrm{{d}}\theta ) - \int h(\theta ) p(\mathrm{{d}}\theta ) \right| < \frac{1}{z} k n^{-p} \end{aligned}$$

and we obtain the second result.

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Christen, J.A., Pérez-Garmendia, J.L. Weak and TV consistency in Bayesian uncertainty quantification using disintegration. Bol. Soc. Mat. Mex. 27, 2 (2021). https://doi.org/10.1007/s40590-021-00317-3

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  • Inverse problems
  • Bayesian inference
  • Disintegration
  • Weak convergence
  • Total variation
  • Discretization consistency

Mathematics Subject Classification

  • 62A99
  • 62C10
  • 35R30