Quantum probability for statisticians; some new ideas
The paper proposes a new foundation for quantum probability based on characterizing theoretical variables as 'accessible' or 'inaccessible' to an observer. It derives a variant of Born's formula linking quantum theory and probability through postulates like the likelihood principle. This framework is then applied to statistical inference, specifically in model reduction and decision theory, suggesting new ways to approach machine learning and parameter estimation.