Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Bayesian networks are a type of probabilistic graphical models lie at the intersection beten statistics and machine learning. They have been shown to be porful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as ll as temporal processes.   review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also,  examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here  survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations beten variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied.
['Concha Bielza', 'Pedro Larrañaga']
I am writing a research paper on a idea Bayesian networks in neuroscience: a survey
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian networks in neuroscience: a survey"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Bayesian networks are a type of probabilistic graphical models lie at the intersection beten statistics and machine learning. They have been shown to be porful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as ll as temporal processes.   review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also,  examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here  survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations beten variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind\u2013morphological, electrophysiological, -omics and neuroimaging\u2013, thereby broadening the scope\u2013molecular, cellular, structural, functional, cognitive and medical\u2013 of the brain aspects to be studied."}]