bayesian_listener.py > function infer
For repetitions > 1, replace the Python loop over target_num with a single batched call to multiple_logpdfs_vec_input_single_cov. Draw all noise at once (target_num × repetitions × P), reshape to (T*R, P), call the Numba kernel once, reshape back.
Potential issue: intermediate loglik array is (T*R, n_templates) — ~5 GB for 793 targets × 50 reps × 2112 templates. May need a chunked fallback for large inputs.
bayesian_listener.py > function infer
For
repetitions > 1, replace the Python loop overtarget_numwith a single batched call tomultiple_logpdfs_vec_input_single_cov. Draw all noise at once (target_num × repetitions × P), reshape to(T*R, P), call the Numba kernel once, reshape back.Potential issue: intermediate
loglikarray is(T*R, n_templates)— ~5 GB for 793 targets × 50 reps × 2112 templates. May need a chunked fallback for large inputs.