Precise Measurement of B Meson Lifetimes with Hadronic Decay Final States

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Damages to the sewerage system by the Tottori-ken seibu Earthquake, 2000

departmental bulletin pape

Performance as a function of number of trials, for both tasks and for all experiments.

Performance was computed as a moving average over test trials (200 trials wide). Shaded regions represent ±1 s.e.m. over subjects. Performance did not change significantly over the course of each experiment.</p

Full model details from The role of familiarity in signaller–receiver interactions

In animal communication, individuals of species exhibiting individual recognition of conspecifics with whom they have repeated interactions, receive signals not only from unfamiliar conspecifics, but also from individuals with whom they have prior experience. Empirical evidence suggests that familiarity with a specific signaller aids receivers in interpreting that signaller's signals, but there has been little theoretical work on this effect. Here, we develop a Bayesian decision-making model and apply it to the well-studied systems of primate ovulation signals. We compare the siring probability of learner males versus non-learner males, based on variation in their assessment of the best time to mate and mate-guard females. We compare males of different dominance ranks, and vary the number of females, and their cycle synchrony. We find strong fitness advantages for learners, which manifest very quickly. Receivers do not have to see the full range of a signaller's signals in order to start gaining familiarity benefits. Reproductive asynchrony and increasing the number of females both enhance learning advantages. We provide theoretical evidence for a strong advantage to specific learning of a signaller's range of signals in signalling systems. Our results have broad implications, not only for understanding communication, but in elucidating additional fitness benefits to group-living, the evolution of individual recognition, and other characteristics of animal behavioural biology

故郷喪失のポーランド文学

departmental bulletin pape

Posterior distributions over parameter values for an example model.

Each subplot represents a parameter of the model. Each colored histogram represents the sampled posterior distribution for a parameter and a subject in experiment 1, with colors consistent for each subject. The limits of the x-axis indicates the allowable range for each parameter. Black triangles indicate the overall mean parameter value.</p

Example analysis of a bootstrapped confidence interval.

(a) Uncertainty estimates for bootstrapped confidence intervals, as a function of the number of subjects included. Blue line represents the median bootstrapped mean of LOO differences, and black lines indicate the lower and upper bounds of the 95% CI. Error bars represent ±1 s.d. (b) For comparison to a, the standard style of plot used to show model comparison results (e.g., Fig 4).</p

Model fits and model comparison for models Fixed and Bayes.

Bayes provides a better fit, but both models have large deviations from the data. Left and middle columns: model fits to mean button press as a function of reliability, true category, and task. Error bars represent ±1 s.e.m. across 11 subjects. Shaded regions represent ±1 s.e.m. on model fits, with each model on a separate row. Right column: LOO model comparison. Bars represent individual subject LOO scores for Bayes, relative to Fixed. Negative (leftward) values indicate that, for that subject, Bayes had a higher (better) LOO score than Fixed. Blue lines and shaded regions represent, respectively, medians and 95% CI of bootstrapped mean LOO differences across subjects. These values are equal to the summed LOO differences reported in the text divided by the number of subjects. Although we plot data as a function of the true category here, the model only takes in measurement and reliability as an input; it is not free to treat individual trials from each true category differently.</p

Distributions of posterior probabilities of being correct, with confidence criteria for Bayesian models with three different levels of strength.

Solid lines represent the distributions of posterior probabilities for each category and task in the absence of measurement noise and sensory uncertainty. Dashed lines represent confidence criteria, generated from the mean of subject 4’s posterior distribution over parameters. Each model has a different number of sets of mappings between posterior probability and confidence report. In BayesUltrastrong, there is one set of mappings. In BayesStrong, there is one set for Task A, and another for Task B. In BayesWeak, as in the non-Bayesian models, there is one set for Task A, and one set for each reported category in Task B. Plots were generated from the mean of subject 4’s posterior distribution over parameters as in Fig 2.</p

Code - simulate

Code - simulat