# Softmax regression bias and prior probabilities for unequal classes

I'm using Softmax regression for a multi-class classification problem. I don't have equal prior probabilities for each of the classes.

I know from Logistic Regression (softmax regression with 2 classes) that the prior probabilities of the classes is implicitly added to the bias ($\log(p_0/p_1)$).

Usually what I do is to manually remove this term from the bias.

My question is, what is the corresponding term in softmax regression bias?

Thanks.

As far as I'm aware, the justification for softmax bias initialization is a bit hand-wavy. Recall softmax regression is maximum (log) likelihood estimation for $W,\textbf{b}$, with the model being the following: $$\DeclareMathOperator{cat}{Cat} \newcommand{\norm}{\left\| #1 \right\|} \newcommand{vsigma}{{\boldsymbol\sigma}} \newcommand{vx}{{\textbf{x}}} \newcommand{vb}{{\textbf{b}}} \newcommand{vz}{{\textbf{z}}} y\sim\cat(\vsigma(W\vx+\vb)); \;\;\;\sigma_i(\vz)=\frac{\exp z_i}{\sum_j\exp z_j}.$$ With bias initialization our intention is to find a good value $\vb$ with which $p(\vx, y|W,\vb)\propto p( y|W,\vb,\vx)$ starts out high. Under the assumption that we initialize $W$ with small near-0 values and that $y$ is a label in $[K]$, $W\vx\approx 0$ so: $$\log p( y|W,\vb,\vx)=\sum_{k=1}^K1_{y=k}\log \sigma_k(W\vx + \vb)\approx\log\sigma_y(\vb)$$ Adding up the log-probabilities for all assumed-independent examples $\{(\vx_i,y_i)\}_{i=1}^n$, a good initialization for $\vb$ would minimize the total approximate data log likelihood: $$\newcommand{vc}{{\textbf{c}}} \sum_{i=1}^n\log\sigma_{y_i}(\vb)=\sum_{i=1}^nb_{y_i}-n\log\sum_{k=1}^K\exp b_k$$ The gradient of the above wrt $\vb$ is $\vc-n\vsigma(\vb)$, with $\vc\in\mathbb{N}^K$ the vector of counts of each class. The function above is also concave, see the question here about smooth max for a proof.
The two facts above imply a maximum is available whenever $\vsigma(\vb)=\vc/n$. This, in turn, suggests a viable initialization for the $i$-th term $b_i$ of the bias $\vb$ is indeed $\log p_i$, the proportion of $i$-labelled examples in the training set (aka the marginal statistics). You might see that you can add any constant to $\vb$ and achieve another likelihood-maximizing bias as well; however, a large scale would get in the way of learning $W$. The relationship with the logistic bias is not coincidental --- this tutorial discusses the similarity.