Non Identifiability

Non-identifiability refers to the inability to uniquely determine model parameters from observed data, a pervasive problem across diverse fields like machine learning, causal inference, and system identification. Current research focuses on mitigating this issue through techniques such as regularization in optimization-based approaches, Bayesian inference, and the development of novel neural network architectures like inVAErt networks for improved parameter estimation. Understanding and addressing non-identifiability is crucial for ensuring the reliability and robustness of models in various applications, ranging from accurate physiological modeling to reliable robot localization and trustworthy causal inference.