This talk will intr oduce novel methodologies for exploring posterior distributions by modifying methodology for exactly (without error) simulating diffusion sample paths . The methodologies discussed have found particula r applicability to "Big Data" problems. We begin b y presenting the Scalable Langevin Exact Algorithm (ScaLE) and recent methodological extensions (inc luding Re-ScaLE\, which avoids the need for partic le approximation in ScaLE)\, which has remarkably good scalability properties as the size of the dat a set increases (it has sub-linear cost\, and pote ntially no cost as a function of data size). ScaLE has particular applicability in the &ldquo\;singl e-core&rdquo\; big data setting - in which inferen ce is conducted on a single computer. In the secon d half of the talk we will present methodology to exactly recombine inferences on separate data sets computed on separate cores - an exact version of &ldquo\;divide and conquer". As such this approach has particu lar applicability in the &ldquo\;mult i-core&rdquo\; big data setting. We conclude by co mmenting on future work on the confluence of these approaches. Joint work with Hongsheng Dai\, Paul Fearnhead\, Adam Johansen\, Divakar Kumar\, Gareth Roberts. LOCATION:Seminar Room 1\, Newton Institute CONTACT:INI IT END:VEVENT END:VCALENDAR