Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Properties of Sensitivity Analysis of Bayesian Belief Networks
Annals of Mathematics and Artificial Intelligence
Parametric Structure of Probabilities in Bayesian Networks
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Sensitivity analysis in Bayesian networks: from single to multiple parameters
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Evidence-invariant sensitivity bounds
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Evidence and scenario sensitivities in naive Bayesian classifiers
International Journal of Approximate Reasoning
Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis
Computational Statistics & Data Analysis
Classical and imprecise probability methods for sensitivity analysis in engineering: A case study
International Journal of Approximate Reasoning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
When do numbers really matter?
Journal of Artificial Intelligence Research
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A distance measure for bounding probabilistic belief change
International Journal of Approximate Reasoning
Making sensitivity analysis computationally efficient
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Analysing sensitivity data from probabilistic networks
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
International Journal of Approximate Reasoning
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Sensitivity analysis in hidden Markov models (HMMs) is usually performed by means of a perturbation analysis where a small change is applied to the model parameters, upon which the output of interest is re-computed. Recently it was shown that a simple mathematical function describes the relation between HMM parameters and an output probability of interest; this result was established by representing the HMM as a (dynamic) Bayesian network. To determine this sensitivity function, it was suggested to employ existing Bayesian network algorithms. Up till now, however, no special purpose algorithms for establishing sensitivity functions for HMMs existed. In this paper we discuss the drawbacks of computing HMM sensitivity functions, building only upon existing algorithms. We then present a new and efficient algorithm, which is specially tailored for determining sensitivity functions in HMMs.