  We compute trivariate probability distributions in the landscape, scanning
simultaneously over the cosmological constant, the primordial density contrast,
and spatial curvature. We consider two different measures for regulating the
divergences of eternal inflation, and three different models for observers. In
one model, observers are assumed to arise in proportion to the entropy produced
by stars; in the others, they arise at a fixed time (5 or 10 billion years)
after star formation. The star formation rate, which underlies all our observer
models, depends sensitively on the three scanning parameters. We employ a
recently developed model of star formation in the multiverse, a considerable
refinement over previous treatments of the astrophysical and cosmological
properties of different pocket universes. For each combination of observer
model and measure, we display all single and bivariate probability
distributions, both with the remaining parameter(s) held fixed, and
marginalized. Our results depend only weakly on the observer model but more
strongly on the measure. Using the causal diamond measure, the observed
parameter values (or bounds) lie within the central $2\sigma$ of nearly all
probability distributions we compute, and always within $3\sigma$. This success
is encouraging and rather nontrivial, considering the large size and dimension
of the parameter space. The causal patch measure gives similar results as long
as curvature is negligible. If curvature dominates, the causal patch leads to a
novel runaway: it prefers a negative value of the cosmological constant, with
the smallest magnitude available in the landscape.
