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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2509.21226 |
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| _version_ | 1866915513869795328 |
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| author | Blackwell, Paul G. |
| author_facet | Blackwell, Paul G. |
| contents | The velocity-jump model is a specific type of piecewise deterministic Markov process in which an individual's velocity is constant except at times that form the events of some point process. It represents an interpretable continuous-time version of the discrete-time `step and turn' models widely used in analysing wildlife telemetry. In this paper, I derive a reversible jump Markov chain Monte Carlo algorithm to carry out exact Bayesian inference for velocity-jump models by reconstructing the trajectories between observations, and illustrate its use in analysing real and simulated telemetry data. The method uses a proposal distribution for updating velocities that is constructed by approximating the movement model with a multivariate normal distribution and then conditioning that distribution on the data. The velocity-jump models considered can incorporate measurement error and Markov dependence between successive velocities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian inference for velocity-jump models for movement Blackwell, Paul G. Methodology The velocity-jump model is a specific type of piecewise deterministic Markov process in which an individual's velocity is constant except at times that form the events of some point process. It represents an interpretable continuous-time version of the discrete-time `step and turn' models widely used in analysing wildlife telemetry. In this paper, I derive a reversible jump Markov chain Monte Carlo algorithm to carry out exact Bayesian inference for velocity-jump models by reconstructing the trajectories between observations, and illustrate its use in analysing real and simulated telemetry data. The method uses a proposal distribution for updating velocities that is constructed by approximating the movement model with a multivariate normal distribution and then conditioning that distribution on the data. The velocity-jump models considered can incorporate measurement error and Markov dependence between successive velocities. |
| title | Bayesian inference for velocity-jump models for movement |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.21226 |