Bayesian Network: Modeling Uncertainty in Robotics Systems
About this ebook
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.
