If your dataset is slightly dense, meaning that most of the events have already been observed and have non-zero counts, you may not need much smoothing at all. In this case, using a very small alpha value, such as 0.1 or 0.01, may be sufficient to improve the estimates without significantly altering the probabilities.

It's also important to note that Laplace smoothing is just one of many smoothing techniques available, and there may be other techniques that are better suited to your specific dataset and application. Some alternative techniques include Lidstone smoothing and Jelinek-Mercer smoothing, among others.

In general, it's a good idea to experiment with different smoothing techniques and values to determine the optimal approach for your specific problem. Cross-validation and other model evaluation techniques can also be used to assess the performance of different smoothing techniques and values.
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