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# Notes on Regularization

In some cases, e.g., if the data is sparse, the iterative algorithms underlying the parameter inference functions might not converge. A pragmatic solution to this problem is to add a little bit of regularization.

Inference functions in choix provide a generic regularization argument: alpha. When \alpha = 0, regularization is turned off; setting \alpha > 0 turns it on. In practice, if regularization is needed, we recommend starting with small values (e.g., 10^{-4}) and increasing the value if necessary.

Below, we briefly how the regularization parameter is used inside the various parameter inference functions.

## Markov-chain based algorithms

For Markov-chain based algorithms such Luce Spectral Ranking and Rank Centrality, \alpha is used to initialize the transition rates of the Markov chain.

In the special case of pairwise-comparison data, this can be loosely understood as placing an independent Beta prior for each pair of items on the respective comparison outcome probability.

## Minorization-maximization algorithms

In the case of Minorization-maximization algorithms, the exponentiated model parameters e^{\theta_1}, \ldots, e^{\theta_n} are endowed each with an independent Gamma prior distribution, with scale \alpha + 1. See Caron & Doucet (2012) for details.

## Other algorithms

The scipy-based optimization functions use an \ell_2-regularizer on the parameters \theta_1, \ldots, \theta_n. In other words, the parameters are endowed each with an independent Gaussian prior with variance 1 / \alpha.

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