To optimize the model structure for the hippocampus, we propose two Bayesian schemes: Bayesian hyperparameter optimization to estimate the unknown electrophysiological properties necessary for constructing a me
Dynamic Causal Modeling DCM is a Bayesian framework for estimating experimentally induced changes in effective connectivity. UnlikeSEMandGranger causality, it seeks to estimate causal influence at the level of the underlying neuronal dynamics, rather than at the level of the observations (e.g., fMRI...
Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’ and therefore, several research efforts have been recently proposed to relax this restriction. However, those ...
Learning temporal causal graphs for relational time-series analysis. In Proceedings of the 27th International Conference on Machine Learning (pp. 687–694), Haifa, Israel. New York: ACM press. (2010). 8. Robinson, J. & Hartemink, A. Non-Stationary Dynamic Bayesian Networks. In Neural ...
Results: In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that ...
2021), which could later be useful for causal reasoning and planning towards a desired state leveraging rewards or motivations derivable from the sensorimotor space given desire. The derivation of the reward signals in the sensorimotor space can be likened to the activity of dopamine, a brain ...
This correlation can assist in a more in-depth analysis of causal relationships and influences among the sensors, thereby enhancing the model performance for anomaly detection in multivariate time series data. In the model proposed in this paper, these embedding vectors are utilized for the following...
One widely used strategy to infer the causal relationship in GRN is to over-express or repress the key TFs and measure the change in global expression. The significantly up- or down-regulated genes may be either directly or indirectly regulated by the perturbed TFs. This strategy has been succ...
Similar to the NOTEARS method, DYNOTEARS extracts the causal structure of the system by minimizing the least square error objective as the score function in the optimization problem. In both of the methods, the square error objective is the distance between the model and the data with the ...
Bayesian Statistics, Chapter Using the Sir Algorithm to Simulate Posterior Distributions Oxford University Press (1998), pp. 395-402 Google Scholar Sengupta et al., under review B. Sengupta, K.J. Friston, W.D. Penny Gradient-based MCMC for dynamic causal modelling Neuroimage (2015) (under re...