#probabilisticgraphicalmodels — Public Fediverse posts

Experiments show LIFAGU achieves near‑zero error vs. ground truth and speeds up inference via lifting, generalizing colour passing to unknown factors. https://hackernoon.com/when-graphs-have-gaps-lifagu-finds-symmetry-and-speeds-up-inference #probabilisticgraphicalmodels

The LIFAGU algorithm transfers potentials from known to unknown factors via structural symmetry, generalizing colour passing and enabling lifted inference. https://hackernoon.com/when-some-factors-go-missing-lifagu-finds-the-symmetries #probabilisticgraphicalmodels

This paper introduces LIFAGU, a generalization of colour passing to lift factor graphs with unknown factors, enabling exact probabilistic inference. https://hackernoon.com/lifagu-lifted-probabilistic-inference-in-factor-graphs-with-unknown-factors #probabilisticgraphicalmodels

I am taking a class on Probabilistic Graphical Models (PGMs) this semester and we have a final project which can be a breadth lit review on a topic or a research project.

Does anyone know about some cool work that combined PGM or PGM methods (e.g., inference, parameter estimation, learning with partial observations, etc.) with #ComputationalNeuroscience or maybe #DecisionMaking models? Ideally with a focus on methods, algorithms, or simulations.

I'm looking for some starting point to dig through the literature a bit and see if anything catches my attention.

Thanks in advance!

#ProbabilisticGraphicalModels #Neuroscience #ExactInference #VariationalInference #CausalInference #SamplingInference #MCMC #ParameterEstimation #StructureLearning #MarkovNetworks #BayesNetworks

I am taking a class on Probabilistic Graphical Models (PGMs) this semester and we have a final project which can be a breadth lit review on a topic or a research project.

Does anyone know about some cool work that combined PGM or PGM methods (e.g., inference, parameter estimation, learning with partial observations, etc.) with #ComputationalNeuroscience or maybe #DecisionMaking models? Ideally with a focus on methods, algorithms, or simulations.

I'm looking for some starting point to dig through the literature a bit and see if anything catches my attention.

Thanks in advance!

#ProbabilisticGraphicalModels #Neuroscience #ExactInference #VariationalInference #CausalInference #SamplingInference #MCMC #ParameterEstimation #StructureLearning #MarkovNetworks #BayesNetworks

I am taking a class on Probabilistic Graphical Models (PGMs) this semester and we have a final project which can be a breadth lit review on a topic or a research project.

Does anyone know about some cool work that combined PGM or PGM methods (e.g., inference, parameter estimation, learning with partial observations, etc.) with #ComputationalNeuroscience or maybe #DecisionMaking models? Ideally with a focus on methods, algorithms, or simulations.

I'm looking for some starting point to dig through the literature a bit and see if anything catches my attention.

Thanks in advance!

#ProbabilisticGraphicalModels #Neuroscience #ExactInference #VariationalInference #CausalInference #SamplingInference #MCMC #ParameterEstimation #StructureLearning #MarkovNetworks #BayesNetworks

I am taking a class on Probabilistic Graphical Models (PGMs) this semester and we have a final project which can be a breadth lit review on a topic or a research project.

Does anyone know about some cool work that combined PGM or PGM methods (e.g., inference, parameter estimation, learning with partial observations, etc.) with #ComputationalNeuroscience or maybe #DecisionMaking models? Ideally with a focus on methods, algorithms, or simulations.

I'm looking for some starting point to dig through the literature a bit and see if anything catches my attention.

Thanks in advance!

#ProbabilisticGraphicalModels #Neuroscience #ExactInference #VariationalInference #CausalInference #SamplingInference #MCMC #ParameterEstimation #StructureLearning #MarkovNetworks #BayesNetworks

I am taking a class on Probabilistic Graphical Models (PGMs) this semester and we have a final project which can be a breadth lit review on a topic or a research project.

Does anyone know about some cool work that combined PGM or PGM methods (e.g., inference, parameter estimation, learning with partial observations, etc.) with #ComputationalNeuroscience or maybe #DecisionMaking models? Ideally with a focus on methods, algorithms, or simulations.

I'm looking for some starting point to dig through the literature a bit and see if anything catches my attention.

Thanks in advance!

#ProbabilisticGraphicalModels #Neuroscience #ExactInference #VariationalInference #CausalInference #SamplingInference #MCMC #ParameterEstimation #StructureLearning #MarkovNetworks #BayesNetworks