Here the conserved total angular momentum is ${\mathbf{J}}_t=(0,0,1)$, while the black-hole spin magnitude remains $\sqrt{3}$. The hole again aligns with the positive $z$ direction in the persistent-torque regime. ThusSince $1-\sqrt{3}<0$, the disk points in the negative $z$ direction and has magnitude $\sqrt{3}-1$. This realizes counteralignment of a black hole and accretion disk even though the hole itself is aligned with the conserved total vector. The black hole and disk do not always finish with aligned spins.

Choose an auxiliary null vector $N$ with $U\cdot N=-1$, parallel-transported along each affine ray. The screen-space projector and its induced positive metric areThey annihilate $U,N$, obey $P^2=P$, and have trace two in four spacetime dimensions. Since both $U$ and $N$ are parallel along $U$, ${\nabla }_{U}P=0$. Equivalently, the screen represents the two-dimensional quotient $U^{\perp }/\mathrm{span}(U)$.

Define the optical matrix of a null congruence byFor a transverse deviation, its screen projection $\zeta =PZ$ satisfies ${\nabla }_U{\zeta }^a={\hat{B}}^a{}_b{\zeta }^b$. Decompose the matrix asThese are the null expansion, null shear and null twist. Null expansion gives the fractional change of a small transverse area, $\theta =d\log (dA)/d\lambda$. Null shear is symmetric trace-free distortion; null twist is the antisymmetric rotation part. This null expansion convention uses the full trace, not half the trace. The auxiliary screen fixes representatives, while the transverse optical content is intrinsic to the null geodesic congruence.

The small baryon-to-photon ratio suppresses nuclear equilibrium abundances through the factor ${\eta }^{A-1}$. Cooling eventually makes the binding-energy exponential favourable, but the deuterium bottleneck delays substantial nuclear burning until well after cosmological weak freeze-out. In ordinary Big Bang nucleosynthesis, nearly every neutron surviving that delay ends up in helium-4. This makes the final primordial helium mass fraction chiefly a neutron budget rather than an unrestricted equilibrium abundance.

Let $r_F$ be the neutron-to-proton ratio at freeze-out, let $\Delta t$ be the delay until effective nuclear burning, and let ${\tau }_n$ be the neutron mean lifetime. The initial fraction of baryons which are neutrons is $x_{n,F}=r_F/(1+r_F)$. Because neutron decay converts a neutron into a proton, total baryon number remains constant, while the neutron fraction becomes $x_{n,B}=x_{n,F}{e}^{-\Delta t/{\tau }_n}$. Each helium nucleus uses two neutrons and four baryons. Hence neutron-limited helium synthesis givesEquivalently, $Y_4=2r_B/(1+r_B)$ with $r_B=x_{n,B}/(1-x_{n,B})$. This accounts for the extra protons produced by neutron decay; multiplying $r_F$ alone by the survival factor would miss their contribution. The following responses refer to ordinary neutron-limited burning, without exotic particle injection or a change to the nuclear network.

Write $\cos {\omega }_{1}t=(e^{i{\omega }_{1}t}+e^{-i{\omega }_{1}t})/2$ and $\Delta \omega ={\omega }_2-{\omega }_1$. Multiplication of the matrix from part (e) givesThese are exactly the constant, difference-frequency, and sum-frequency terms in the PDF, with each Hermitian pair visible. For a weak, slowly varying envelope, the terms at $2{\omega }_1$ and ${\omega }_1+{\omega }_2$ average out by the rotating-wave approximation. To discard the other transition as well, its detuning must be nonzero and large compared with the drive coupling and envelope bandwidth. Sufficient scale conditions arewhere $B$ is a characteristic envelope bandwidth; turn-on transients must satisfy the same spectral restrictions. ThenSmall coupling compared with the optical frequencies alone is not enough for this selective approximation: if $|\Delta \omega |$ is comparable with the pulse bandwidth or Rabi scale, the spectator transition remains driven. A long selective gate also accumulates a small off-resonant Stark phase of order $(A_1{d}_2{)}^2/\Delta \omega$, so that error must be acceptable or compensated. These assumptions distinguish resonant quantum control from an exact decoupling.

Under independent censoring conditional on the modelled covariates, an observed event contributes $f_i(x_i)=h_i(x_i)e^{-H_i(x_i)}$, and a right-censored observation contributes $S_i(x_i)=e^{-H_i(x_i)}$. Factors from the censoring distribution may be omitted when they contain no survival-model parameters. The survival likelihood and log-likelihood are thereforeCensored individuals still contribute their observed exposure through $H_i(x_i)$; they are not deleted from the analysis.

Assess the observation-error assumption with standardized residual plots and quantile-quantile plots, preferably comparing the same discrepancies with posterior predictive replicated data. Inspect tail behavior, asymmetry, variance versus fitted size and serial patterns within a tree; fitted residuals are dependent and do not behave exactly like independent errors.

This does not test normality of the tree random effects. For that, simulate new tree-specific log asymptotes from the population hierarchy and compare between-tree discrepancies or use leave-one-tree-out predictions. There are only five trees, so distributional checks on their population are intrinsically weak. Conditioning on the five fitted effects can hide misspecification of their normal population law.

Model A has fifteen separate curve parameters plus one error-scale parameter. With such broad priors, one might have expected more of those sixteen parameters to be effectively estimated, rather than $p_D=12$, and might also have expected its greater unpooled flexibility to give better fit than the displayed $\overline{D}=248.1$. Model B instead has a smaller mean deviance despite sharing information between trees.

This is a diagnostic concern, not a mathematical impossibility. Effective parameter count in DIC depends on posterior curvature and parameterization, while mean posterior deviance is not minimized deviance. A weakly identified nonlinear asymptote–slope trade-off, skewness, multiple posterior regions or slow Markov chain Monte Carlo mixing could explain the unexpected comparison. For A, the implied deviance at the posterior mean is $\overline{D}-p_D=236.1$; for B it is $230.3$. Inspect traces, effective sample sizes, independent chains and fitted curves, and extend sampling where needed. Fifty thousand iterations alone do not establish convergence.

Smaller mean Bayesian deviance indicates better average likelihood fit. Model B has the smallest $\overline{D}$, improving on A by $3.9$ and on C by $3.1$. Model C has the smallest effective parameter count in DIC, reflecting its shared timing and growth coefficients. Model B's hierarchical covariance parameters and residual flexibility contribute to a larger effective count than C.

Adding fit and complexity gives the reported deviance information criterion: A has $260.1$, B $258.1$, and C $255.6$. Thus DIC favors Model C, whose simpler shape more than compensates for its slightly worse mean fit than B. The differences are modest, and the criteria must use the same observation likelihood and deviance convention. These numbers are not posterior model probabilities; predictive assessment remains useful.

Keep a tree-specific log asymptote $a_i$ with a normal population distribution, but use one shared location $b$ and one shared positive growth coefficient $k$. For example, with fixed prior constants chosen on meaningful scales:

mean.a ~ dnorm(m.a, precision.a.prior)
sd.a ~ dnorm(0, precision.sd.a) T(0,)
precision.a <- 1 / pow(sd.a, 2)
b ~ dnorm(m.b, precision.b.prior)
k ~ dnorm(m.k, precision.k.prior) T(0,)
sigma ~ dnorm(0, precision.sigma.prior) T(0,)
precision.y <- 1 / pow(sigma, 2)
for (i in 1:5) {
    a[i] ~ dnorm(mean.a, precision.a)
    for (j in 1:7) {
        fitted[i,j] <- exp(a[i]) / (1 + exp(-b - k*t[j]))
        Y[i,j] ~ dnorm(fitted[i,j], precision.y)
    }
}

Here the transformed days t[j] are supplied as data. This is Model C: only the log asymptote is a random effect, while location and growth rate are shared. The prior constants are fixed inputs, and the truncations give positive standard deviations and an increasing growth curve. One can retain the original error-precision prior for a comparison matching the supplied fits; the code above illustrates a more interpretable positive-scale alternative.

A common second parameter means the trees reach the same fraction of their asymptotic sizes at the centered day; a common third parameter means they have the same standardized growth rate. The trees may differ mainly in final size while sharing a growth shape. Similar environment and the overlapping posterior estimates make this biologically plausible. Equality is a stronger modelling assumption than partial pooling, and should be checked predictively.

Fit both hierarchies to the historical data and compare their posterior predictive checks. Use discrepancies sensitive to between-individual tails, such as the maximum standardized personal mean, tail counts and the spread of individual sample means. Simulate new individual means as well as repeated observations; conditioning only on fitted personal means would conceal a poor population model.

Leave whole individuals out for cross-validation, rather than just measurements, when the aim is prediction for a new individual. Compare held-out predictive log densities or interval calibration, with uncertainty in the comparison. Check both fits' Markov chain Monte Carlo convergence diagnostics and sensitivity to scale priors. Better tail prediction and adequate calibration would support the heavier-tailed model; the data are required to decide, so no empirical winner can be asserted here.

The Student t-distribution has heavier tails than the normal distribution, so unusual personal means need not force the entire population's spread upward or be shrunk as strongly toward its center. The latent ${\lambda }_i$ allows a large local prior variance for an outlying individual. This normal scale mixture representation of Student t is a useful robust version of the hierarchical Bayesian model. It still assumes a symmetric, unimodal population; a genuinely multimodal population may need a different model.

Conditionally on ${\lambda }_i$, the proposed precision gives ${\theta }_i\mid {\lambda }_i\sim N(\mu ,4{\tau }^2/{\lambda }_i)$. Thus, with independent $Z\sim N(0,1)$ and ${\lambda }_i\sim {\chi }_4^2$,By the Student t-distribution construction, $\boxed{({\theta }_i-\mu )/\tau \sim t_4}$. Here $\tau$ is a scale rather than the standard deviation: the prior variance of ${\theta }_i$ is $2{\tau }^2$.

Add the new individual's latent mean and log variance to the same population hierarchy. Enter only that individual's first two measurements as observations; use the historical data and these two values to fit the model. The third measurement must be held out, or the predictive check reuses the value it is meant to test. For example, augment the unchanged population hyperpriors with this WinBUGS structure:

new.mean ~ dnorm(mu, invtau2)
new.logvar ~ dnorm(phi, invpsi2)
new.precision <- exp(-new.logvar)
for (k in 1:2) {
    new.Y[k] ~ dnorm(new.mean, new.precision)
}
future.Y ~ dnorm(new.mean, new.precision)
upper.indicator <- step(future.Y - held.out.third)

future.Y is unobserved. Its simulated values give the posterior predictive distribution, including uncertainty in the population parameters and personal variance. A central 99.9% prediction interval uses its $0.0005$ and $0.9995$ empirical quantiles. Flag the third value if it is outside that interval. Equivalently, estimate its predictive tail probability by the mean of upper.indicator and flag if that mean is below $0.0005$ or above $0.9995$. This is two-sided, unlike the earlier one-sided upper limit.

The hierarchical Bayesian model separates variability between individuals from variability between repeated measurements on an individual. In WinBUGS, dnorm takes a precision parameter, so inverse variance is the appropriate second argument.

Line (1) gives $\log {\sigma }_j^2\sim N(\varphi ,{\psi }^2)$, hence a positive log-normal distribution for each within-individual variance. Estimating the shared parameters supplies partial pooling of these variances without forcing them equal. Line (2) assigns a proper bounded flat prior to the between-individual standard deviation $\tau$, and line (3) does the same for the standard deviation $\psi$ on the log-variance scale. The broad bounded priors on $\mu$ and $\varphi$ are also proper.

This is a reasonable structure for heterogeneous repeated measurements, but flat does not mean uninformative on all transformed scales. The bounds must be wide enough for plausible values, while such enormous ranges can put substantial mass on implausible predictions. Use prior predictive checks and sensitivity analysis, and check the Markov chain Monte Carlo convergence diagnostics. Serial dependence or systematic trends would require an extension of the independent-measurement likelihood.

After $k$ measurements the normal-normal conjugacy formulas areThe upper monitoring limit is $m_{ik}+z_{0.999}\sqrt{v_{ik}+{\sigma }_i^2}$. Its center moves toward the athlete's own mean and the population's influence diminishes. Its predictive width decreases toward $z_{0.999}{\sigma }_i$, since uncertainty about the mean vanishes but the next measurement's error remains. The absolute upper limit need not decrease monotonically: its center can move upward or downward with the observations. This is sequential normal predictive monitoring.

Taking the upper $0.999$ quantile of the posterior predictive distribution givesThe same predictive false-alarm probability is retained after updating with the first measurement.

Model the count relative to exposure, for exampleHere $E_i$ is a measured exposure such as patient-days, $x_i$ contains hospital predictors, and $b_i$ is a random effect. This Poisson regression separates hospital size from rate variation and supplies partial pooling. Use proper, scale-appropriate priors on coefficients and variation; add regional or temporal effects if the data demand them. A gamma mixing law with an estimated shape is another way to allow more flexible overdispersion. Compare the fitted model's counts and dispersion through posterior predictive checks.

The model treats hospitals as exchangeable and assumes the same exponential mixing law for their total expected annual counts. Its shape is fixed: the prior standard deviation equals its mean. Real hospitals differ in size, patient mix, ascertainment and observation effort, so expected counts should not generally share one distribution without accounting for these predictors. Common regional conditions can also invalidate their assumed conditional independence. The resulting geometric distribution for observed counts is a restrictive distributional prediction that can be checked.

The gamma full conditional distribution has mean $(y_i+1)/(1+\psi )=(y_i+1)\varphi$. The tower property and the beta distribution mean giveWriting $\overline{y}=s/n$ and $w=s/(s+n)=\overline{y}/(1+\overline{y})$ yieldsThus partial pooling pulls every local mean toward the overall sample mean. The common weight arises from the fixed shape-one mixing distribution; it is the Poisson–exponential posterior shrinkage formula.