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Bayesian Network - Modeling Uncertainty in Robotics Systems - cover

Bayesian Network - Modeling Uncertainty in Robotics Systems

Fouad Sabry

Casa editrice: One Billion Knowledgeable

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Sinossi

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.
Disponibile da: 16/12/2024.
Lunghezza di stampa: 491 pagine.

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