“…Extracting latent nonlinear dynamics from observed time-series data is important for understanding the dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data that assumes the existence of latent variables that cannot be observed directly [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. State space models are used in various fields to forecast observation values [ 7 , 15 , 22 ] and to estimate latent variables [ 11 , 20 , 21 , 26 ].…”