  We propose an efficient meta-algorithm for Bayesian estimation problems that
is based on low-degree polynomials, semidefinite programming, and tensor
decomposition. The algorithm is inspired by recent lower bound constructions
for sum-of-squares and related to the method of moments. Our focus is on sample
complexity bounds that are as tight as possible (up to additive lower-order
terms) and often achieve statistical thresholds or conjectured computational
thresholds.
  Our algorithm recovers the best known bounds for community detection in the
sparse stochastic block model, a widely-studied class of estimation problems
for community detection in graphs. We obtain the first recovery guarantees for
the mixed-membership stochastic block model (Airoldi et el.) in constant
average degree graphs---up to what we conjecture to be the computational
threshold for this model. We show that our algorithm exhibits a sharp
computational threshold for the stochastic block model with multiple
communities beyond the Kesten--Stigum bound---giving evidence that this task
may require exponential time.
  The basic strategy of our algorithm is strikingly simple: we compute the
best-possible low-degree approximation for the moments of the posterior
distribution of the parameters and use a robust tensor decomposition algorithm
to recover the parameters from these approximate posterior moments.
