Binomial distributions capture the probabilities of `heads' outcomes when a (biased) coin is tossed multiple times. The coin may be identified with a distribution on the two-element set 0,1, where the 1 outcome corresponds to `head'. One can also toss two separate coins, with different biases, in parallel and record the outcomes. This paper investigates a slightly different `bivariate' binomial distribution, where the two coins are dependent (also called: entangled, or entwined): the two-coin is a distribution on the product 0,1 x 0,1. This bivariate binomial exists in the literature, with complicated formulations. Here we use the language of category theory to give a new succint formulation. This paper investigates, also in categorically inspired form, basic properties of these bivariate distributions, including their mean, variance and covariance, and their behaviour under convolution and under updating, in Laplace's rule of succession. Furthermore, it is shown how Expectation Maximisation works for these bivariate binomials, so that mixtures of bivariate binomials can be recognised in data. This paper concentrates on the bivariate case, but the binomial distributions may be generalised to the multivariate case, with multiple dimensions, in a straightforward manner.
Inference is a fundamental reasoning technique in probability theory. When applied to a large joint distribution, it involves updating with evidence (conditioning) in one or more components (variables) and computing the outcome in other components. When the joint distribution is represented by a Bayesian network, the network structure may be exploited to proceed in a compositional manner -- with great benefits. However, the main challenge is that updating involves (re)normalisation, making it an operation that interacts badly with other operations. String diagrams are becoming popular as a graphical technique for probabilistic (and quantum) reasoning. Conditioning has appeared in string diagrams, in terms of a disintegration, using bent wires and shaded (or dashed) normalisation boxes. It has become clear that such normalisation boxes do satisfy certain compositional rules. This paper takes a decisive step in this development by adding a removal rule to the formalism, for the deletion of shaded boxes. Via this removal rule one can get rid of shaded boxes and terminate an inference argument. This paper illustrates via many (graphical) examples how the resulting compositional inference technique can be used for Bayesian networks, causal reasoning and counterfactuals. 21 pages, 2 figures. To be published in MFPS 2025 proceedings
We adapt Fiore, Plotkin, and Turi's treatment of abstract syntax with binding, substitution, and holes to account for languages with second-class sorts. These situations include programming calculi such as the Call-by-Value lambda-calculus (CBV) and Levy's Call-by-Push-Value (CBPV). Prohibiting second-class sorts from appearing in variable contexts changes the characterisation of the abstract syntax from monoids in monoidal categories to actions in actegories. We reproduce much of the development through bicategorical arguments. We apply the resulting theory by proving substitution lemmata for varieties of CBV.
We introduce the concept of compact quantitative equational theory. A quantitative equational theory is defined to be compact if all its consequences are derivable by means of finite proofs. We prove that the theory of interpolative barycentric (also known as convex) quantitative algebras of Mardare et. al. is compact. This serves as a paradigmatic example, used to obtain other compact quantitative equational theories of convex algebras, each axiomatizing some distance on finitely supported probability distributions.
