Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review
S Hosseini, D Ivanov - Expert systems with applications, 2020 - Elsevier
In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that
possess unique methodical features to model dependencies in complex networks, such as …
possess unique methodical features to model dependencies in complex networks, such as …
[图书][B] Bayesian networks: an introduction
T Koski, J Noble - 2011 - books.google.com
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and
applications of Bayesian networks, a topic of interest and importance for statisticians …
applications of Bayesian networks, a topic of interest and importance for statisticians …
A review on indoor human aware autonomous mobile robot navigation through a dynamic environment survey of different path planning algorithm and methods
Practical realistic environment for path and continuous motion planning problems normally
consist of numerous working areas such as in indoor application consist of number of …
consist of numerous working areas such as in indoor application consist of number of …
Aligning robot and human representations
To act in the world, robots rely on a representation of salient task aspects: for example, to
carry a coffee mug, a robot may consider movement efficiency or mug orientation in its …
carry a coffee mug, a robot may consider movement efficiency or mug orientation in its …
Aligning human and robot representations
To act in the world, robots rely on a representation of salient task aspects: for example, to
carry a coffee mug, a robot may consider movement efficiency or mug orientation in its …
carry a coffee mug, a robot may consider movement efficiency or mug orientation in its …
Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences
Data science has primarily focused on big data, but for many physics, chemistry, and
engineering applications, data are often small, correlated and, thus, low dimensional, and …
engineering applications, data are often small, correlated and, thus, low dimensional, and …
Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept
The aeronautics industry is attempting to implement important changes to its maintenance
strategy. The article presents a new framework for making final decision on aeroplane …
strategy. The article presents a new framework for making final decision on aeroplane …
A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks
The classification of surrounding rock quality is critical for the dynamic construction and
design of tunnels. However, obtaining complete parameters for predicting the surrounding …
design of tunnels. However, obtaining complete parameters for predicting the surrounding …
The trade-off between morphology and control in the co-optimized design of robots
Conventionally, robot morphologies are developed through simulations and calculations,
and different control methods are applied afterwards. Assuming that simulations and …
and different control methods are applied afterwards. Assuming that simulations and …
pgmpy: A Python Toolkit for Bayesian Networks
Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision
making. pgmpy is a python package that provides a collection of algorithms and tools to …
making. pgmpy is a python package that provides a collection of algorithms and tools to …