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On Rules of Probability Theory Explained:
Example comment on the first article. This comment also demonstrates the ability to format text and insert code with auto-highlight like: class ProbabilityRules: @staticmethod def sum_rule(p_XY): p_X = {} for X, Y in p_XY: p_X[X] = p_X.get(X, 0) + p_XY[(X, Y)] return p_X @staticmethod def product_rule(p_Y_given_X, p_X): p_XY = {} for Y, X in p_Y_given_X: p_XY[(X, Y)] = p_Y_given_X[(Y, X)] * p_X[X] return p_XY @staticmethod def bayes_theorem(p_X_given_Y, p_Y, p_X): p_Y_given_X = {} for X, Y in p_X_given_Y: p_Y_given_X[(Y, X)] = (p_X_given_Y[(X, Y)] * p_Y[Y]) / p_X[X] return p_Y_given_XAlso, I can add formulae by wrapping them in a single or double '$' symbols: $$p(X = x_i) = \sum_{j=1}^{L} p(X = x_i, Y = y_j)$$
class ProbabilityRules: @staticmethod def sum_rule(p_XY): p_X = {} for X, Y in p_XY: p_X[X] = p_X.get(X, 0) + p_XY[(X, Y)] return p_X @staticmethod def product_rule(p_Y_given_X, p_X): p_XY = {} for Y, X in p_Y_given_X: p_XY[(X, Y)] = p_Y_given_X[(Y, X)] * p_X[X] return p_XY @staticmethod def bayes_theorem(p_X_given_Y, p_Y, p_X): p_Y_given_X = {} for X, Y in p_X_given_Y: p_Y_given_X[(Y, X)] = (p_X_given_Y[(X, Y)] * p_Y[Y]) / p_X[X] return p_Y_given_X