The numerator of the posterior, and what is actually intractable

Chapter 4 notes, for the binary response model: labels \(t_n\in\{0,1\}\), weights \(\mathbf{w}\), sigmoid likelihood and a Gaussian prior. An interactive page works through every step below with sliders for \(\mathbf{w}\) and \(\sigma^2\).

Bayes’ rule for the weights

The numerator is \(g(\mathbf{w}) = p(\mathbf{t}\mid\mathbf{X},\mathbf{w})\,p(\mathbf{w}\mid\sigma^2)\) and the denominator \(Z\) is the marginal likelihood. The book writes the factor that multiplies \(g\) as \(Z^{-1}\): \(Z\) is the normaliser (the integral of \(g\)), and the posterior divides by it. This is just a naming convention. Both \(Z\) and \(1/Z\) are equally unknown here.

Evaluating the numerator at one w

Pick a specific weight vector. Two numbers multiply:

1. The prior (two weights, so \(D=2\)). It depends only on \(\mathbf{w}\) and \(\sigma^2\), not on the data:

1. The likelihood. For each data point compute \(z_n=\mathbf{w}^T\mathbf{x}_n\) and \(P_n=1/(1+e^{-z_n})\), then take the probability the model gives to the observed label, \(P_n\) if \(t_n=1\) and \(1-P_n\) if \(t_n=0\). Multiply over all \(n\):

Multiply the two to get \(g(\mathbf{w})\). A product of many numbers below 1 becomes tiny, so in practice add logs: \(\log g = \log\text{likelihood}+\log\text{prior}\).

A weight vector whose boundary separates the classes correctly gives each point a high probability for its own label, so the likelihood is near 1. A weight vector that points the wrong way gives most points a low probability, and the likelihood collapses. The prior then penalises large weights.

The numerator is not the intractable part

\(g(\mathbf{w})\) is a completely known function: give it any \(\mathbf{w}\) and it returns a number. Evaluated for every \(\mathbf{w}\) it is the shape of the posterior, only on the wrong vertical scale. With two weights you can evaluate it on a grid of \((w_1,w_2)\) and draw a heat map or contours.

What is intractable is turning that function into what Bayesian inference needs. Each of these is an integral over \(\mathbf{w}\):

• the normaliser \(Z=\int g(\mathbf{w})\,d\mathbf{w}\),
• the posterior mean and covariance, \(\mathbb{E}[\mathbf{w}]=\tfrac1Z\int\mathbf{w}\,g(\mathbf{w})\,d\mathbf{w}\),
• the marginal of one weight, \(p(w_1\mid\text{data})=\tfrac1Z\int g(w_1,w_2)\,dw_2\),
• predictions, \(P(t_{\text{new}}=1\mid\mathbf{x}_{\text{new}},\text{data})=\tfrac1Z\int\sigma(\mathbf{w}^T\mathbf{x}_{\text{new}})\,g(\mathbf{w})\,d\mathbf{w}\),
• a way to draw samples from \(g/Z\).

None has a closed form here, because the product of sigmoid factors with a Gaussian is not a recognisable density, so there are no formulas for its integrals or moments.

The conjugate contrast. In the coin example \(g\) was proportional to a beta density. Recognising the family gave the mean, the variance and a sampler for free. Knowing \(g\) pointwise was not what made that case tractable. Belonging to a family with known integrals was.

Why a grid is not the answer in general. With two weights, summing \(g\) over a grid gives rough values for \(Z\) and for the posterior mean. With \(D\) weights, the number of grid points needed grows like (points per axis)\(^D\), which quickly becomes impossible.

How the three methods use only pointwise g

• MAP: maximise \(g\) (the unknown \(Z\) does not depend on \(\mathbf{w}\), so it does not move the maximum).
• Laplace: fit a Gaussian using the location, height and curvature of \(g\) at its peak.
• Metropolis–Hastings: compare \(g\) at two points. The unknown \(Z\) cancels in the ratio, and the samples then estimate every integral above by averaging.

The heat map of \(g\) on the interactive page shows why the peak and the mean differ: the surface is skewed, so its centre of mass is not at its peak, and a symmetric Gaussian at the peak (Laplace) cannot capture that.

The decision boundary in terms of the weights

The boundary is where \(P(t=1)=0.5\), i.e. \(\mathbf{w}^T\mathbf{x}=0\):

• Slope \(=-w_1/w_2\) and intercept \(=0\): with no constant term, the boundary always passes through the origin.
• \(\mathbf{w}\) is perpendicular to the boundary and points toward the class-1 side.
• Multiplying \(\mathbf{w}\) by a positive constant leaves the line unchanged. It only changes how quickly the probability goes from 0 to 1 across it, which is why \(\sigma^2\) affects the confidence of a prior draw but not its direction.
• A non-zero intercept needs a constant input: with \(\mathbf{x}=(1,x_1,x_2)\) and weights \((w_0,w_1,w_2)\) the boundary is \(x_2=-\tfrac{w_1}{w_2}x_1-\tfrac{w_0}{w_2}\).