Bayesian Network - Modeling Uncertainty in Robotics Systems
Fouad Sabry
Editora: One Billion Knowledgeable
Sinopse
1: Bayesian network: Delve into the foundational concepts of Bayesian networks and their applications. 2: Statistical model: Explore the framework of statistical models crucial for data interpretation. 3: Likelihood function: Understand the significance of likelihood functions in probabilistic reasoning. 4: Bayesian inference: Learn how Bayesian inference enhances decisionmaking processes with data. 5: Pattern recognition: Investigate methods for recognizing patterns in complex data sets. 6: Sufficient statistic: Discover how sufficient statistics simplify data analysis while retaining information. 7: Gaussian process: Examine Gaussian processes and their role in modeling uncertainty. 8: Posterior probability: Gain insights into calculating posterior probabilities for informed predictions. 9: Graphical model: Understand the structure and utility of graphical models in representing relationships. 10: Prior probability: Study the importance of prior probabilities in Bayesian reasoning. 11: Gibbs sampling: Learn Gibbs sampling techniques for efficient statistical sampling. 12: Maximum a posteriori estimation: Discover MAP estimation as a method for optimizing Bayesian models. 13: Conditional random field: Explore the use of conditional random fields in structured prediction. 14: Dirichletmultinomial distribution: Understand the Dirichletmultinomial distribution in categorical data analysis. 15: Graphical models for protein structure: Investigate applications of graphical models in bioinformatics. 16: Exponential family random graph models: Delve into exponential family random graphs for network analysis. 17: Bernstein–von Mises theorem: Learn the implications of the Bernstein–von Mises theorem in statistics. 18: Bayesian hierarchical modeling: Explore hierarchical models for analyzing complex data structures. 19: Graphoid: Understand the concept of graphoids and their significance in dependency relations. 20: Dependency network (graphical model): Investigate dependency networks in graphical model frameworks. 21: Probabilistic numerics: Examine probabilistic numerics for enhanced computational methods.
Disponível desde: 16/12/2024.
Comprimento de impressão: 491 páginas.
