SUSY Before the Next Lepton Collider

After a brief review of the Minimal Supersymmetric Standard Model (MSSM) and specifically the Minimal Supergravity Model (SUGRA), the prospects for discovering and studying SUSY at the CERN Large Hadron Collider are reviewed. The possible role for a future Lepton Collider --- whether $\mu^+\mu^-$ or $e^+e^-$ --- is also discussed.Comment: LaTeX with included aipproc.sty, 17 pages, 12 figures. To appear in Workshop on Physics at the First Muon Collide

Search for SUSY at LHC: Precision Measurements

Methods to make precision measurements of SUSY masses and parameters at the CERN Large Hadron Collider are described.Comment: 7 pages, Latex using included prhep97.sty, 9 figures; To appear in the Proceedings of the International Europhysics Conference on High Energy Physics (Jerusalem, 1997

Sleptons at a First Muon Collider

Signatures for sleptons, which have been extensively studied for the Next Linear Collider, are reexamined taking into account some of the different features of a First Muon Collider.Comment: LaTeX with included aipproc.sty, 7 pages, 5 figures, to appear in Workshop on Physics at the First Muon Collide

A Compilation Target for Probabilistic Programming Languages

Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermediate representation language we call probabilistic C, which itself can be compiled to machine code by standard compilers and linked to operating system libraries yielding an efficient, scalable, portable probabilistic programming compilation target. This opens up a new hardware and systems research path for optimizing probabilistic programming systems.Comment: In Proceedings of the 31st International Conference on Machine Learning (ICML), 201