pyhs3.distributions.LogProbTerms¶
- class pyhs3.distributions.LogProbTerms(per_event=<factory>, channel=<factory>, constraints=<factory>)[source]¶
Structured per-channel contributions to
pyhs3.Model.log_prob.Distributions describe what they contribute via
Distribution.log_prob_terms(); the model owns the channel-dataset pairing and decides how the pieces enter the joint log-likelihood (event weighting, summing over events, global constraint deduplication).- per_event¶
Log-density terms with the observable as a free pt.vector input, broadcasting to shape (N, M) for N events and parameter batch size M. The model sums these over the event axis (applying per-event weights when present).
- channel¶
Scalar terms added once per channel, broadcasting onto the (M,) parameter batch axis (e.g. the -nu yield term of an extended mixture).
- constraints¶
Log-terms keyed by factor name, depending only on scalar nuisance parameters. The model adds each name exactly once globally, so constraints shared across channels are not double-counted.
- Parameters:
- __init__(per_event=<factory>, channel=<factory>, constraints=<factory>)¶
Methods
__init__([per_event, channel, constraints])Attributes