How to define a Posterior distribution

The recommended way to define a posterior distribution in CUQIpy is to use the JointDistribution class to define the joint distribution of the parameters and the data and then condition on observed data to obtain the posterior distribution as shown in the examples below.

import cuqi
import numpy as np

A simple Bayesian inverse problem

Consider a deconvolution inverse problem given by

\[\mathbf{y} = \mathbf{A}\mathbf{x}.\]

See Deconvolution1D for more details.

A, y_obs, _ = cuqi.testproblem.Deconvolution1D().get_components()

Then consider the following Bayesian model

\[\begin{split}\begin{align*} \mathbf{x} &\sim \mathcal{N}(\mathbf{0}, 0.1\,\mathbf{I})\\ \mathbf{y} &\sim \mathcal{N}(\mathbf{A}\mathbf{x}, 0.05^2\,\mathbf{I}) \end{align*}\end{split}\]

which can be written in CUQIpy as

x = cuqi.distribution.Gaussian(np.zeros(A.domain_dim), 0.1)
y = cuqi.distribution.Gaussian(A(x), 0.05**2)

The joint distribution \(p(\mathbf{x}, \mathbf{y})\) is then obtained by

joint = cuqi.distribution.JointDistribution(x, y)
print(joint)

JointDistribution(
    Equation:
        p(x,y) = p(x)p(y|x)
    Densities:
        x ~ CUQI Gaussian.
        y ~ CUQI Gaussian. Conditioning variables ['x'].
)

The posterior \(p(\mathbf{x}|\mathbf{y}=\mathbf{y}^\mathrm{obs})\) is obtained by conditioning on the observed data as follows.

posterior = joint(y=y_obs)
print(posterior)

Posterior(
    Equation:
         p(x|y) ∝ L(x|y)p(x)
    Densities:
        y ~ CUQI Gaussian Likelihood function. Parameters ['x'].
        x ~ CUQI Gaussian.
 )

Evaluating the posterior log density is then as simple as

posterior.logd(np.ones(A.domain_dim))

array([-21527.9713636])

Posterior with two forward models

Suppose we had two forward models \(\mathbf{A}\) and \(\mathbf{B}\):

\[\begin{split}\begin{align*} \mathbf{y} &= \mathbf{A}\mathbf{x}\\ \mathbf{d} &= \mathbf{B}\mathbf{x}\\ \end{align*}\end{split}\]

# Both observations come from the same unknown x
A, y_obs, _ = cuqi.testproblem.Deconvolution1D().get_components()
B, d_obs, _ = cuqi.testproblem.Deconvolution1D(PSF="Defocus", noise_std=0.02).get_components()

Then consider the following Bayesian model

\[\begin{split}\begin{align*} \mathbf{x} &\sim \mathcal{N}(\mathbf{0}, 0.1\,\mathbf{I})\\ \mathbf{y} &\sim \mathcal{N}(\mathbf{A}\mathbf{x}, 0.05^2\mathbf{I})\\ \mathbf{d} &\sim \mathcal{N}(\mathbf{B}\mathbf{x}, 0.01^2\mathbf{I}) \end{align*}\end{split}\]

x = cuqi.distribution.Gaussian(np.zeros(A.domain_dim), 0.1)
y = cuqi.distribution.Gaussian(A(x), 0.05**2)
d = cuqi.distribution.Gaussian(B(x), 0.01**2)

The joint distribution \(p(\mathbf{x}, \mathbf{y}, \mathbf{d})\) is then obtained by

joint2 = cuqi.distribution.JointDistribution(x, y, d)
print(joint2)

JointDistribution(
    Equation:
        p(x,y,d) = p(x)p(y|x)p(d|x)
    Densities:
        x ~ CUQI Gaussian.
        y ~ CUQI Gaussian. Conditioning variables ['x'].
        d ~ CUQI Gaussian. Conditioning variables ['x'].
)

The posterior \(p(\mathbf{x}|\mathbf{y}=\mathbf{y}^\mathrm{obs},\mathbf{d}=\mathbf{d}^\mathrm{obs})\) is obtained by conditioning on the observed data as follows.

posterior2 = joint2(y=y_obs, d=d_obs)
print(posterior2)

MultipleLikelihoodPosterior(
    Equation:
        p(x|y,d) ∝ p(x)L(x|y)L(x|d)
    Densities:
        x ~ CUQI Gaussian.
        y ~ CUQI Gaussian Likelihood function. Parameters ['x'].
        d ~ CUQI Gaussian Likelihood function. Parameters ['x'].
)

Evaluating the posterior log density is then as simple as

posterior2.logd(np.ones(A.domain_dim))

array([-565491.2570644])

Arbitrarily complex posterior distributions

The JointDistribution class can be used to construct arbitrarily complex posterior distributions. For example suppose we have the following 3 forward models

\[\begin{split}\begin{align*} \mathbf{y} &= \mathbf{A}\mathbf{x}\\ \mathbf{d} &= \mathbf{B}\mathbf{x}\\ \mathbf{b} &= C(\mathbf{x}) \end{align*}\end{split}\]

where \(C\) is a nonlinear function.

# Same x for all 3 observations
A, y_obs, _ = cuqi.testproblem.Deconvolution1D().get_components()
B, d_obs, _ = cuqi.testproblem.Deconvolution1D(PSF="Defocus", noise_std=0.02).get_components()
C = cuqi.model.Model(lambda x: np.linalg.norm(x)**2, 1, A.domain_dim)
b_obs = 16

Then consider the following Bayesian model

\[\begin{split}\begin{align*} q &\sim \mathcal{U}(0.1, 10)\\ l &\sim \mathrm{Gamma}(1, 1)\\ s &\sim \mathrm{Gamma}(1, 10^{-2})\\ \mathbf{x} &\sim \mathcal{N}(\mathbf{0}, l^{-1}\mathbf{I})\\ \mathbf{y} &\sim \mathcal{N}(\mathbf{A}\mathbf{x}, s^{-1}\mathbf{I})\\ \mathbf{d} &\sim \mathcal{N}(\mathbf{B}\mathbf{x}, 0.01\mathbf{I})\\ \mathbf{b} &\sim \mathcal{L}(\mathbf{C}(\mathbf{x}), q) \end{align*}\end{split}\]

q = cuqi.distribution.Uniform(0.1, 10)
l = cuqi.distribution.Gamma(1, 1)
s = cuqi.distribution.Gamma(1, 1e-2)
x = cuqi.distribution.Gaussian(np.zeros(A.domain_dim), lambda l: 1/l)
y = cuqi.distribution.Gaussian(A(x), lambda s: 1/s)
d = cuqi.distribution.Gaussian(B(x), 0.01**2)
b = cuqi.distribution.Laplace(C(x), lambda q: q)

The joint distribution \(p(q, l, s, \mathbf{x}, \mathbf{y}, \mathbf{d}, \mathbf{b})\) is then obtained by

joint3 = cuqi.distribution.JointDistribution(q, l, s, x, y, d, b)
print(joint3)

JointDistribution(
    Equation:
        p(q,l,s,x,y,d,b) = p(q)p(l)p(s)p(x|l)p(y|s,x)p(d|x)p(b|q,x)
    Densities:
        q ~ CUQI Uniform.
        l ~ CUQI Gamma.
        s ~ CUQI Gamma.
        x ~ CUQI Gaussian. Conditioning variables ['l'].
        y ~ CUQI Gaussian. Conditioning variables ['x', 's'].
        d ~ CUQI Gaussian. Conditioning variables ['x'].
        b ~ CUQI Laplace. Conditioning variables ['x', 'q'].
)

The posterior \(p(q, l, s, \mathbf{x}|\mathbf{y}=\mathbf{y}^\mathrm{obs},\mathbf{d}=\mathbf{d}^\mathrm{obs},\mathbf{b}=\mathbf{b}^\mathrm{obs})\) is obtained by conditioning on the observed data as follows.

posterior3 = joint3(y=y_obs, d=d_obs, b=b_obs)
print(posterior3)

JointDistribution(
    Equation:
        p(q,l,s,x|y,d,b) ∝ p(q)p(l)p(s)p(x|l)L(s,x|y)L(x|d)L(q,x|b)
    Densities:
        q ~ CUQI Uniform.
        l ~ CUQI Gamma.
        s ~ CUQI Gamma.
        x ~ CUQI Gaussian. Conditioning variables ['l'].
        y ~ CUQI Gaussian Likelihood function. Parameters ['x', 's'].
        d ~ CUQI Gaussian Likelihood function. Parameters ['x'].
        b ~ CUQI Laplace Likelihood function. Parameters ['x', 'q'].
)

Evaluating the posterior log density jointly over p, l, s, and \(\mathbf{x}\) is then as simple as

posterior3.logd(q=1, l=1, s=1, x=np.ones(A.domain_dim))

array([-541824.04845684])