In this study, we propose a Bayesian model that can jointly estimate the number of senses of words and their changes through time.The model combines a dynamic topic model on Gaussian Markov random fields (Frermann and Lapata, 2016) with a logistic stick-breaking process that realizes the Dirichlet process.In the experiments, we evaluated the proposed model in terms of interpretability, accuracy in estimating the number of senses, and tracking their changes using both artificial data and real data.We quantitatively verified that the model behaves as expected through evaluation using artificial data.Using the CCOHA corpus, we showed that our model outperforms the baseline model and investigated the semantic changes of several well-known target words.