The Indian Buffet Process defines a distribution on binary matrices with a finite number of rows (e.g. objects) and an infinite number of columns (e.g. features).
(define (ibp-stick-breaking-process concentration base-measure) (let ((sticks (mem (lambda j (random-beta 1.0 concentration)))) (atoms (mem (lambda j (base-measure))))) (lambda () (let loop ((j 1) (dualstick (sticks 1))} (append (if (flip dualstick) ;; with prob. dualstick (atoms j) ;; add feature j ’()) ;; otherwise, next stick (loop (+ j 1) (* dualstick (sticks (+ j 1))))))))))
This procedure does not halt, and therefore does not induce a well-defined distribution on values, although the original IBP does. This raises the question of whether the IBP has a computable de Finetti representation and may have implications for sampler design.