gemlib.mcmc.discrete_time_state_transition_model.move_events

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gemlib.mcmc.discrete_time_state_transition_model.move_events#

gemlib.mcmc.discrete_time_state_transition_model.move_events(incidence_matrix, transition_index, num_units, delta_max, count_max, name=None)#

Move partially-censored transition events

This kernel provides an MCMC algorithm that moves partially-censored transition events in a DiscreteTimeStateTransitionModel event timeseres. It caters for the situation in which a transition is known to have occurred (e.g. as a result of a subsequent case detection), but the time at which it occurred is unknown.

Parameters:

  • incidence_matrix (Array) – the state-transition graph incidence matrix

  • transition_index (int) – the index of the transition in incidence_matrix to update.

  • num_units (int) – the number of epidemiological units to update at once

  • delta_max (int) – the maximum time interval over which to move transition times.

  • count_max (int) – the maximum number of transitions to move at once.

  • name (str | None) – the name of the kernel.

Returns:

An instance of SamplingAlgorithm with members init(tlp_fn: LogProbFnType, position: Position, initial_conditions: Tensor) and step(tlp_fn: LogProbFnType, cks: ChainAndKernelState, seed: SeedType, initial_conditions: Tensor). initial_conditions is an extra keyword argument required to be passed to the init and step functions.

References

Philip D O’Neill and Gareth O Roberts (1999) Baysian inference for partially observed stochastic epidemics. _Journal of the Royal Statistical Society: Series A (Statistics in Society), 162:121–129.

Jewell et al. (2023) Bayesian inference for high-dimensional discrete- time epidemic models: spatial dynamics of the UK COVID-19 outbreak. Pre-print arXiv:2306.07987

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