Denis D. Mauá's
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    • Neuro Probabilistic Logic Programming
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    • Investigating Universal Adversarial Attacks Against Transformers-Based Automatic Essay Scoring Systems
    • dPASP: A Probabilistic Logic Programming Environment For Neurosymbolic Learning and Reasoning
    • A Compositional Atlas for Algebraic Circuits
    • A New Benchmark for Automatic Essay Scoring in Portuguese
    • Probabilistic Logic Programming under the L-Stable Semantics
    • Specifying credal sets with probabilistic answer set programming
    • Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks
    • On Modal Clustering with Gaussian Sum-Product Networks
    • Differentiable Planning for Optimal Liquidation
    • Differentiable Planning with Indefinite Horizon
    • Distinct Outcomes in COVID-19 Patients with Positive or Negative RT-PCR Test
    • Exploration Versus Exploitation in Model-Based Reinforcement Learning: An Empirical Study
    • Integrating Question Answering and Text-to-SQL in Portuguese
    • Time Robust Trees: Using Temporal Invariance to Improve Generalization
    • Timing to Intubation COVID-19 Patients: Can We Put It Off until Tomorrow?
    • Tractable Classification with Non-Ignorable Missing Data Using Generative Random Forests
    • Tractable Mode-Finding in Sum-Product Networks with Gaussian Leaves
    • Cautious Classification with Data Missing Not at Random using Generative Random Forests
    • Efficient algorithms for Risk-Sensitive Markov Decision Processes with limited budget
    • Fast And Accurate Learning of Probabilistic Circuits by Random Projections
    • Learning Probabilistic Sentential Decision Diagrams under Logic Constraints by Sampling and Averaging
    • A Contact Network-Based Approach for Online Planning of Containment Measures for COVID-19
    • Complexity results for probabilistic answer set programming
    • Decision-Aware Model Learning for Actor-Critic Methods: When Theory Does Not Meet Practice
    • Efficient Algorithms for Robustness Analysis of Maximum A Posteriori Inference in Selective Sum-Product Networks
    • Finding Feasible Policies for Extreme Risk-Averse Agents in Probabilistic Planning
    • Learning Probabilistic Sentential Decision Diagrams by Sampling
    • On the Performance of Planning Through Backpropagation
    • Prediction of Environmental Conditions for Maritime Navigation using a Network of Sensors: A Practical Application of Graph Neural Networks
    • The Joy of Probabilistic Answer Set Programming: Semantics, complexity, expressivity, inference
    • Thirty years of credal networks: Specification, algorithms and complexity
    • Tractable inference in credal sentential decision diagrams
    • Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks
    • Deep Reactive Policies for Planning in Stochastic Nonlinear Domains
    • End-To-End Imitation Learning of Lane Following Policies Using Sum-Product Networks
    • Exploring the Space of Probabilistic Sentential Decision Diagrams
    • Robust Analysis of MAP Inference in Selective Sum-Product Networks
    • Speeding up parameter and rule learning for acyclic probabilistic logic programs
    • The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws
    • The Finite Model Theory of Bayesian Networks: Descriptive Complexity
    • Advances in Automatically Solving the ENEM
    • Robustifying sum-product networks
    • The complexity of Bayesian networks specified by propositional and relational languages
    • When a Robot Reaches Out for Human Help
    • Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
    • Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
    • Closed-Form Solutions in Learning Probabilistic Logic Programs by Exact Score Maximization
    • Credal Sum-Product Networks
    • Modeling Markov Decision Processes with Imprecise Probabilities Using Probabilistic Logic Programming
    • On the complexity of propositional and relational credal networks
    • On the Semantics and Complexity of Probabilistic Logic Programs
    • On Using Sum-Product Networks For Multi-Label Classification
    • Parameter Learning in ProbLog with Probabilistic Rules
    • Speeding-up ProbLog's Parameter Learning
    • The Complexity of Inferences and Explanations in Probabilistic Logic Programming
    • The Descriptive Complexity of Bayesian Network Specifications
    • The effect of combination functions on the complexity of relational Bayesian networks
    • University Entrance Exam as a Guiding Test for Artificial Intelligence
    • The Complexity of Bayesian Networks Specified by Propositional and Relational Languages
    • Better Initialization Heuristics for Order-based Bayesian Network Structure Learning
    • Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams
    • Fast local search methods for solving limited memory influence diagrams
    • Hidden Markov models with set-valued parameters
    • Improving Acyclic Selection Order-Based Bayesian Network Structure Learning
    • Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution
    • Probabilistic Graphical Models Specified by Probabilistic Logic Programs: Semantics and Complexity
    • The Effect of Combination Functions on the Complexity of Relational Bayesian Networks
    • The structure and complexity of credal semantics
    • The Well-Founded Semantics of Cyclic Probabilistic Logic Programs
    • A Tractable Class of Model Counting Problems
    • Bayesian Networks of Bounded Treewith: A Performance Analysis
    • Bayesian Networks Specified Using Propositional and Relational Constructs: Combined, Data, and Domain Complexity
    • DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Model
    • Early classification of time series by hidden Markov models with set-valued parameters
    • Initialization Heuristics for Greedy Bayesian Network Structure Learning
    • On the Complexity of Propositional and Relational Credal Networks
    • Specifying Probabilistic Relational Models with Description Logics
    • The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages
    • The Complexity of Plate Probabilistic Models
    • Advances in Learning Bayesian Networks of Bounded Treewidth
    • Advances in Learning Bayesian Networks of Bounded Treewidth
    • Algorithms for Hidden Markov Models With Imprecisely Specified Parameters
    • Equivalences between Maximum a Posteriori Inference in Bayesian Networks and Maximum Expected Utility Computation in Influence Diagrams
    • Probabilistic Inference in Credal Networks: New Complexity Results
    • Speeding Up k-Neighborhood Local Search in Limited Memory Influence Diagrams
    • Trading off Speed and Accuracy in Multilabel Classification
    • Algorithms and Complexity Results for Discrete Probabilistic Reasoning Tasks
    • An Ensemble of Bayesian Networks for Multilabel Classification
    • On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables
    • On the Complexity of Strong and Epistemic Credal Networks
    • Anytime Marginal MAP Inference
    • Evaluating credal classifiers by utility-discounted predictive accuracy
    • Solving Limited Memory Influence Diagrams
    • The Complexity of Approximately Solving Influence Diagrams
    • Updating Credal Networks is Approximable in Polynomial Time
    • Solving Limited Memory Influence Diagrams
    • A Fully Polynomial Time Approximation Scheme for Updating Credal Networks of Bounded Treewidth and Number of Variable States
    • Solving Decision Problems with Limited Information
    • Utility-Based Accuracy Measures to Empirically Evaluate Credal Classifiers
    • Representing and Classifying User Reviews
    • Topic models on the automatic classification of user reviews
    • Managing Trust in Virtual Communities with Markov Logic
    • Using Social Data to Predict Trust on Web Communities : A Case Study with the Epinions.com Website
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Hidden Markov models with set-valued parameters

Jan 1, 2016·
Denis Deratani Mauá
,
Alessandro Antonucci
,
Cassio Polpo De Campos
· 0 min read
PDF Cite DOI
Type
2
Publication
Neurocomputing
Last updated on Jan 1, 2016
← Fast local search methods for solving limited memory influence diagrams Jan 1, 2016
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