Reasoning with dynamic networks in practice

Several mahemtaical models have been treated among which there has been a preference on Bayesian networks and their use in modelling and handling uncertainty.

[1]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[2]  S. Pauker,et al.  The Markov Process in Medical Prognosis , 1983, Medical decision making : an international journal of the Society for Medical Decision Making.

[3]  J R Beck,et al.  Markov Models in Medical Decision Making , 1993, Medical decision making : an international journal of the Society for Medical Decision Making.

[4]  Ron Kohavi,et al.  Improving simple Bayes , 1997 .

[5]  B. Craig,et al.  Estimation of the transition matrix of a discrete-time Markov chain. , 2002, Health economics.

[6]  Geoffrey Zweig,et al.  Speech Recognition with Dynamic Bayesian Networks , 1998, AAAI/IAAI.

[7]  Theodore Charitos,et al.  Smoothed Particle Filtering for Dynamic Bayesian Networks , 2006, ECAI.

[8]  Daphne Koller,et al.  Expectation Propagation for Continuous Time Bayesian Networks , 2005, UAI.

[9]  Theodore Charitos,et al.  Computing short‐interval transition matrices of a discrete‐time Markov chain from partially observed data , 2008, Statistics in medicine.

[10]  Philippe Weber,et al.  Reliability modelling with dynamic bayesian networks , 2003 .

[11]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[12]  Vladimir Pavlovic,et al.  Multimodal Speaker Detection Using Input/Output Dynamic Bayesian Networks , 2000, ICMI.

[13]  Ewart R. Carson,et al.  A Model-Based Approach to Insulin Adjustment , 1991, AIME.

[14]  Luis Enrique Sucar,et al.  Probabilistic temporal reasoning and its application to fossil power plant operation , 1998 .

[15]  Peter J. F. Lucas,et al.  A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU , 2000, Artif. Intell. Medicine.

[16]  George R. Bergus,et al.  Medical Decision Making: Medical Decision Making , 2008 .

[17]  Theodore Charitos,et al.  A discrete kernel sampling algorithm for DBNs , 2006 .

[18]  Milos Hauskrecht,et al.  Planning treatment of ischemic heart disease with partially observable Markov decision processes , 2000, Artif. Intell. Medicine.

[19]  Linda C. van der Gaag,et al.  Properties of Sensitivity Analysis of Bayesian Belief Networks , 2002, Annals of Mathematics and Artificial Intelligence.

[20]  Frederik J. A. Beek,et al.  The predictive value of angiographic results for the outcome of percutaneous transluminal angioplasty in stenosed femoral bypass grafts , 1997, CardioVascular and Interventional Radiology.

[21]  E. J. Sondik,et al.  The Optimal Control of Partially Observable Markov Decision Processes. , 1971 .

[22]  Daphne Koller,et al.  Sampling in Factored Dynamic Systems , 2001, Sequential Monte Carlo Methods in Practice.

[23]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[24]  José Mira Mira,et al.  NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time , 2002, Artif. Intell. Medicine.

[25]  David Madigan,et al.  Probabilistic Temporal Reasoning , 2005, Handbook of Temporal Reasoning in Artificial Intelligence.

[26]  B A Craig,et al.  Estimating AIDS-free survival in a severely immunosuppressed asymptomatic HIV-infected population in the era of antiretroviral triple combination therapy. Swiss HIV Cohort Study. , 1999, Journal of acquired immune deficiency syndromes and human retrovirology : official publication of the International Retrovirology Association.

[27]  Silja Renooij,et al.  Analysing Sensitivity Data from Probabilistic Networks , 2001, UAI.

[28]  Alexander Kreinin,et al.  Regularization Algorithms for Transition Matrices , 2001 .

[29]  Silja Renooij,et al.  How to Elicit Many Probabilities , 1999, UAI.

[30]  Kathryn B. Laskey.  Sensitivity analysis for probability assessments in Bayesian networks , 1995, IEEE Trans. Syst. Man Cybern..

[31]  L. C. van der Gaag,et al.  A Dynamic Model for Therapy Selection in ICU Patients with VAP , 2006 .

[32]  Linda C. van der Gaag,et al.  Sensitive Analysis for Threshold Decision Making with Bayesian Belief Networks , 1999, AI*IA.

[33]  Keiji Kanazawa,et al.  A model for reasoning about persistence and causation , 1989 .

[34]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[35]  P. Poupart.  Exploiting structure to efficiently solve large scale partially observable Markov decision processes , 2005 .

[36]  Yoshua Bengio,et al.  Input-output HMMs for sequence processing , 1996, IEEE Trans. Neural Networks.

[37]  Adnan Darwiche,et al.  Constant-space reasoning in dynamic Bayesian networks , 2001, Int. J. Approx. Reason..

[38]  Linda C. van der Gaag,et al.  Sensitivity Analysis of Markovian Models , 2006, FLAIRS.

[39]  Daphne Koller,et al.  Continuous Time Bayesian Networks , 2012, UAI.

[40]  Lain L. MacDonald,et al.  Hidden Markov and Other Models for Discrete- valued Time Series , 1997 .

[41]  Adnan Darwiche,et al.  A distance measure for bounding probabilistic belief change , 2002, Int. J. Approx. Reason..

[42]  J. Kingman.  The imbedding problem for finite Markov chains , 1962 .

[43]  Gregory F. Cooper,et al.  A Method for Using Belief Networks as Influence Diagrams , 2013, UAI 1988.

[44]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[45]  Nando de Freitas,et al.  Sequential Monte Carlo in Practice , 2001 .

[46]  Kevin P. Murphy,et al.  The Factored Frontier Algorithm for Approximate Inference in DBNs , 2001, UAI.

[47]  Avi Pfeffer,et al.  Asynchronous Dynamic Bayesian Networks , 2005, UAI.

[48]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[49]  Stuart J. Russell,et al.  Local Learning in Probabilistic Networks with Hidden Variables , 1995, IJCAI.

[50]  Silja Renooij,et al.  Probabilistic Networks as Probabilistic Forecasters , 2003, AIME.

[51]  Joydeep Ghosh,et al.  A New Formulation of Coupled Hidden Markov Models , 2001 .

[52]  Linda C. van der Gaag,et al.  Convergence in Markovian models with implications for efficiency of inference , 2007, Int. J. Approx. Reason..

[53]  Xavier Boyen,et al.  Exploiting the Architecture of Dynamic Systems , 1999, AAAI/IAAI.

[54]  Enrique F. Castillo,et al.  Sensitivity analysis in discrete Bayesian networks , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[55]  Eugene Santos,et al.  Probabilistic temporal networks: A unified framework for reasoning with time and uncertainty , 1999, Int. J. Approx. Reason..

[56]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[57]  Paul Dagum,et al.  Time series prediction using belief network models , 1995, Int. J. Hum. Comput. Stud..

[58]  Adnan Darwiche,et al.  When do Numbers Really Matter? , 2001, UAI.

[59]  Richard Dearden,et al.  Building Hybrid Rover Models: Lessons Learned , 2004 .

[60]  Ross D. Shachter,et al.  Dynamic programming and influence diagrams , 1990, IEEE Trans. Syst. Man Cybern..

[61]  Milos Hauskrecht,et al.  Value-Function Approximations for Partially Observable Markov Decision Processes , 2000, J. Artif. Intell. Res..

[62]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[63]  Linda C. van der Gaag,et al.  Sensitivity Properties of Markovian Models , 2004 .

[64]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[65]  Victor Y. Pan,et al.  Solving a Polynomial Equation: Some History and Recent Progress , 1997, SIAM Rev..

[66]  James R. Cuthbert,et al.  The Logarithm Function for Finite‐State Markov Semi‐Groups , 1973 .

[67]  Matthew Brand,et al.  Coupled hidden Markov models for modeling interacting processes , 1997 .

[68]  Pierre Baldi,et al.  Hidden Markov Models of the G-Protein-Coupled Receptor Family , 1994, J. Comput. Biol..

[69]  Petar M. Djuric,et al.  Resampling Algorithms for Particle Filters: A Computational Complexity Perspective , 2004, EURASIP J. Adv. Signal Process..

[70]  Nicholas J. Higham,et al.  Approximating the Logarithm of a Matrix to Specified Accuracy , 2000, SIAM J. Matrix Anal. Appl..

[71]  Silja Renooij,et al.  Probabilities for a probabilistic network: a case study in oesophageal cancer , 2002, Artif. Intell. Medicine.

[72]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[73]  Dan Svenstrup,et al.  Monte Carlo methods for dynamical systems , 2009 .

[74]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[75]  S. Johansen,et al.  Some Results on the Imbedding Problem for Finite Markov Chains , 1974 .

[76]  G. Brier,et al.  Some applications of statistics to meteorology , 1958 .

[77]  Bruce D'Ambrosio,et al.  Real-Time Inference with Large-Scale Temporal Bayes Nets , 2002, UAI.

[78]  Bo Thiesson,et al.  ARMA Time-Series Modeling with Graphical Models , 2004, UAI.

[79]  Eric Horvitz,et al.  Dynamic Network Models for Forecasting , 1992, UAI.

[80]  D. M. Titterington,et al.  Improved Particle Filters and Smoothing , 2001, Sequential Monte Carlo Methods in Practice.

[81]  Avi Pfeffer,et al.  Sufficiency, Separability and Temporal Probabilistic Models , 2001, UAI.

[82]  Francisco Javier Díez,et al.  Networks of probabilistic events in discrete time , 2002, Int. J. Approx. Reason..

[83]  Stuart J. Russell,et al.  Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.

[84]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[85]  Linda C. van der Gaag,et al.  Sensitivity Analysis for Threshold Decision Making with Dynamic Networks , 2006, UAI.

[86]  Christian Musso,et al.  Improving Regularised Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[87]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[88]  Marek J. Druzdzel,et al.  Efficient stochastic sampling algorithms for bayesian networks , 2001 .

[89]  Adnan Darwiche,et al.  A differential approach to inference in Bayesian networks , 2000, JACM.

[90]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[91]  Peter J. F. Lucas,et al.  A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients , 2009, Expert Syst. Appl..

[92]  N. Welton,et al.  Estimation of Markov Chain Transition Probabilities and Rates from Fully and Partially Observed Data: Uncertainty Propagation, Evidence Synthesis, and Model Calibration , 2005, Medical decision making : an international journal of the Society for Medical Decision Making.

[93]  Uffe Kjærulff,et al.  dHugin: a computational system for dynamic time-sliced Bayesian networks , 1995 .

[94]  Ross D. Shachter.  Probabilistic Inference and Influence Diagrams , 1988, Oper. Res..

[95]  J. Kassirer,et al.  The threshold approach to clinical decision making. , 1980, The New England journal of medicine.

[96]  Stuart J. Russell,et al.  The BATmobile: Towards a Bayesian Automated Taxi , 1995, IJCAI.

[97]  Linda C. van der Gaag,et al.  Making Sensitivity Analysis Computationally Efficient , 2000, UAI.

[98]  J. Rosenthal,et al.  Finding Generators for Markov Chains via Empirical Transition Matrices, with Applications to Credit Ratings , 2001 .

[99]  Ann E. Nicholson,et al.  Dynamic Belief Networks for Discrete Monitoring , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[100]  G. Reuter,et al.  Spectral theory for the differential equations of simple birth and death processes , 1954, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[101]  Gregory M. Provan,et al.  Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams , 1993, UAI.

[102]  Michael I. Jordan,et al.  Probabilistic Independence Networks for Hidden Markov Probability Models , 1997, Neural Computation.

[103]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[104]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[105]  S. Homan,et al.  Determining Transition Probabilities , 1994, Medical decision making : an international journal of the Society for Medical Decision Making.

[106]  Niels Peek,et al.  Explicit temporal models for decision-theoretic planning of clinical management , 1999, Artif. Intell. Medicine.

[107]  Xavier Boyen,et al.  Tractable Inference for Complex Stochastic Processes , 1998, UAI.

[108]  Constantin F. Aliferis,et al.  A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques , 1996, UAI.

[109]  Jeffrey S. Simonoff,et al.  Smoothing categorical data , 1995 .

[110]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[111]  R. Hogg,et al.  Improved survival among HIV-infected individuals following initiation of antiretroviral therapy. , 1998, JAMA.

[112]  Richard W. Madsen,et al.  Markov Chains: Theory and Applications , 1976 .

[113]  Colin Aitken,et al.  An Application of Discrete Kernel Methods to Forensic Odontology , 1979 .

[114]  Nikos A. Vlassis,et al.  Perseus: Randomized Point-based Value Iteration for POMDPs , 2005, J. Artif. Intell. Res..

[115]  Adnan Darwiche,et al.  Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters , 2004, UAI.

[116]  Craig Boutilier,et al.  Context-Specific Independence in Bayesian Networks , 1996, UAI.

[117]  Adnan Darwiche,et al.  Inference in belief networks: A procedural guide , 1996, Int. J. Approx. Reason..

[118]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[119]  Irving John Good,et al.  The Estimation of Probabilities: An Essay on Modern Bayesian Methods , 1965 .

[120]  Avi Pfeffer,et al.  Approximate Separability for Weak Interaction in Dynamic Systems , 2006, UAI.

[121]  Linda C. van der Gaag,et al.  Speeding Up Inference in Markovian Models , 2005, FLAIRS.

[122]  Marc J M Bonten,et al.  Prevention of infection in the intensive care unit , 2004, Current opinion in critical care.

[123]  Michael I. Jordan,et al.  Probabilistic Networks and Expert Systems , 1999 .

[124]  Michael Luby,et al.  Approximating Probabilistic Inference in Bayesian Belief Networks is NP-Hard , 1993, Artif. Intell..

[125]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[126]  Francisco Javier Díez,et al.  Modelling Dynamic Causal Interactions with Bayesian Networks: Temporal Noisy Gates , 2000 .