When comparing graphical models, there are various methods and metrics to choose from, depending on the type and purpose of the comparison. For instance, likelihood-based methods measure how well ...

Graphical models come in two main types, each with its own strengths and weaknesses for predicting complex phenomena: . Directed Graphical Models Bayesian networks are a popular type of DGM, ...

They introduce Neural Graphical Models (NGMs), a novel type of PGM that leverages deep neural networks to learn and efficiently represent probability functions over a domain. What sets NGMs apart is ...

Mainly, there are two types of Graph models: Bayesian Graph Models: These models consist of Directed-Cyclic Graph(DAG) and there is always a conditional probability associated with the random ...

Generative models have gained much popularity in recent years. These models help in handling missing information as well as treating with the variable-length sequences. Technically speaking, ...

We show that some graphical models with no hidden variables including Bayesian networks with several families of local distributions are Curved Exponential Families (CEFs). We also show that Baysian ...

In our paper, “Neural Graphical Models (opens in new tab),” presented at ECSQARU 2023 (opens in new tab), we propose Neural Graphical Models (NGMs), a new type of PGM that learns to represent the ...

Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomplete and scattered.

Various types of autonomous vehicles(AVs) are used widely in the field of military and civilian. Aiming at the difficulty of the real-time intelligent planning of the AVs in the dynamic and uncertain ...