This book presents a summary of recent work on the interface between artificial intelligence and statistics. It does this through a series of papers by different authors working in different areas of this interface. These papers are a selected and referenced subset of papers presented at the 3rd Interntional Workshop on Artificial Intelligence and Statistics, Florida, January 1991.

D.J. Hand is Professor of Statistics at the Open University, UK.

List of contributors Introduction D.J. Hand -- PART ONE Statistical expert systems -- 1 DEXPERT: an expert system for the design of experiments /T.J. Lorenzen, L.T. Truss, W.S. Spangler, W.T. Corpus and A.B. Parker -- 2 Inside two commercially available statistical expert systems /J.F.M. Raes -- 3 AMIA: Aide a la Modelisation par l'lntelligence Artificielle (expert system for simulation modelling and sectoral forecasting) /M. Ollivier, R. Arrus, JML-A. Durillon, S. Robert and B. Debord -- 4 An architecture for knowledge-based statistical support systems /A. Prat, E. Edmonds, J.M. Catot, J. Lores, J. Galmes and P. Fletcher -- 5 Enhancing explanation capabilities of statistical expert systems through hypertext /P. Hietala -- 6 Measurement scales as metadata D.J. Hand /PART TWO Belief networks -- 7 On the design of belief networks for knowledge-based systems /B. Abramson -- 8 Lack-of-information based control in graphical belief systems -- 9 Adaptive importance sampling for Bayesian networks applied to filtering problems /A.R. Runnalls -- 10 Intelligent arc addition, belief propagation and utilization of parallel processors by probabilistic inference engines /A. Ranjbar and M. McLeish -- 11 A new method for representing and solving Bayesian decision problems /P.P. Shenoy -- PART THREE Learning -- 12 Inferring causal structure in mixed populations /C. Glymour, P. Spirtes and R. Scheines -- 13 A knowledge acquisition inductive system guided by empirical interpretation of derived results /K. Tsujino and S. Nishida -- 14 Incorporating statistical techniques into empirical symbolic learning systems /F. Esposito, D. Malerba and G. Semeraro -- 15 Learning classification trees /W. Buntine -- 16 An analysis of two probabilistic model induction techniques /S.L. Crawford and M. Fung -- PART FOUR Neural networks -- 17 A robust back propagation algorithm for function approximation /D.S. Chen and R.C. Jain -- 18 Maximum likelihood training of neural networks /H. Gish -- 19 A connectionist knowledge acquisition tool: CONKAT /A. Ultsch, R. Mantyk and G. Halmans -- 20 Connectionist, rule-based, and Bayesian decision aids: an empirical comparison /S. Schwartz, J. Wiles, I. Gough and S. Phillips -- PART FIVE Text manipulation -- 21 Statistical approaches to aligning sentences and identifying word correspondences in parallel texts: a report on work in progress /W.A. Gale and K.W. Church -- 22 Probabilistic text understanding /R.P. Goldman and E. Charniak -- 23 The application of machine learning techniques in subject classification /I. Kavanagh, C. Ward and J. Dunnion -- PART SIX Other areas -- 24 A statistical semantics for causation /J. Pearl and T.S. Verma -- 25 Admissible stochastic complexity models for classification problems /P. Smyth -- 26 Combining the probability judgements of experts: statistical and artificial intelligence approaches /LA. Cox -- 27 Randomness and independence in non-monotonic reasoning /E. Neufeld -- 28 Consistent regions in probabilistic logic when using different norms /D. Bouchaffra -- 29 A decision theoretic approach to controlling the cost of planning /L. Hartman -- Index.