In Bayesian statistics you have to choose a prior distribution for the parameters to combine with the data to get a posterior distribution. Choosing a tight prior, assuming that the parameters should live in a particular space, reduces the impact of the data on the posterior estimates. This is just like regularisation in machine learning where adding a penalty to the loss function prevents over-fitting. This is more than just an analogy, and this article will explore a couple of cases with constant regression and classification.

A typical machine learning approach to regression is to minimise the root mean squared error. A probabilistic perspective for this is to consider the regression \( y = f_\theta(X) + \epsilon \), where y is the outcome, X are the predictors, \( f_\theta \) is a function parameterised by \(\theta\), and $\epsilon$ is the error term. If we assume that \(\epsilon \in N(0, \sigma^2)\) is normally distributed, this is equivalent to saying that \( y \in N(f_\theta^2(X), \sigma^2) \). We then need to pick the most likely parameters \(\theta\) given the data.

The Bayesian perspective on this is if we have a prior on the parameters \( p(\theta) \), and data \(X_i, y_i\) then the posterior estimate is \( p(\theta \vert X_i, y_i) = \frac{p(X_i, y_i \vert \theta) p(\theta)}{p(\{X_i,y_i\}_i)} \). In Bayesian statistics we estimate the whole distribution, but we can focus on the maximum likelihood estimator, the value of \(\theta\) that maximises the posterior probability. Since the logarithm is a monotonic function, the maximum likelihood occurs as the same point as the maximum log likelihood. Taking the logarithm and plugging in the normal distribution for \( p(X,y \vert \theta) \) gives \( l(\theta, \sigma) = -\frac{1}{2\sigma^2} \sum_{i=1}^{N} (f_{\theta}(X_i) - y_i)^2 + \log(p(\theta)) - N \log(\sigma) + c \) for some constant c. In the case of a flat prior, \( p(\theta) = 1 \) then the maximum likelihood estimator is equivalent to minimising the (root) mean squared error. However in general the prior acts as a regularisation; for example if we take a prior that the parameters are normally distributed it reduces to Tikhonov Regularisation. However we could pick other prior distributions to recover an Lᵖ regularisation, and in particular a Laplace distribution recovers the LASSO.

There's more here, in Bayesian statistics people tend to use a Horseshoe Prior instead of a Laplace Distribution, and Michael Betancourt has an article on my reading list on Bayes Sparse Regression that goes through the trade-offs with different regularising priors.

# Binary classification

Similar ideas can be applied in Binary Classification, here the metric is typically Binary Cross Entropy. From a probabilistic perspective we can assume the data comes from a Binomial distribution \( y \in B(f_\theta(X)) \). Here \( p(X_i, y_i \vert \theta) = f_\theta(X_i)^{y_i} (1 - f_\theta(X_i))^{1-y_i}\) (keeping in mind that \(y_i\) can only take the values 0 or 1). Then, as in the normal regression case, we can find the maximum likelihood estimator by minimising the log likelihood \( l(\theta) = \sum_{y_i = 1} \log(f_\theta (X_i)) + \sum_{y_i=0} \log(1 - f_\theta(X_i) + \log(p(\theta)) + c\). With a flat prior this maximising the log likelihood is equivalent to minimising the Binary Cross Entropy.

Consider in particular the constant model \( f_\theta(X_i) = \theta \), where this reduces to \(l(\theta) = s \log(\theta) + (N-s) \log(1-\theta) + \log(p(\theta)) \), where s is the number of successes and N is the total number of trials. A bit of calculus and algebra shows that with a flat prior this is maximised when \( \hat{\theta} = \frac{s}{N} \).

One problem with this is the variance of the binomial is \( \sqrt{\frac{\theta(1-\theta)}{N}} \), and so if we have 0 or N successes the maximum likelihood estimate for the variance is 0, which in most cases isn't right - we're not going to be exactly zero. A method for handling this, which I learned in the book Regression and Other Stories, is to set a prior of \({\rm Beta}(3,3)\) which is equivalent to adding 4 extra trials with 2 successes. Then the maximum likely estimate for the parameter is \( \hat{\theta} = \frac{s+2}{N+4} \) and the variance will always be non-zero.

In the log likelihood this adds a penalty of \( \log(\theta^2 (1-\theta)^2) + c'\), for some constant \(c'\). Rewriting \( \psi = \theta - \frac{1}{2}\) and rearranging gives the penalty, up to a constant, as \( 2 \log(\frac{1}{4} - \psi^2)\). For small \(\psi\) we can do a Taylor expansion to get \(-8 \psi^2 = -8 (\theta - \frac{1}{2})^2 \). So this transformation is similar to a \(l^2\) penalty (I suspect this is for the same reason a Binomial converges to a Gaussian for large samples and moderate probabilities).

What's interesting here is the Beta prior gives a more reasonable and understandable regularisation than \(l^2\) regularisation, especially for probabilities close to 0 or 1. I would never have thought of a log Beta penalty, but thinking of it as a prior it makes really good sense. On the other hand being able to switch to a maximum likelihood, and thinking of the prior as a penalty, makes things much quicker to calculate than trying to estimate the whole posterior. There's a Wikipedia article on Bayesian interpretation of Kernel Regularisation It's useful being able to switch between the two viewpoints.