We annnounce a proof of the fact that a K-stable Fano manifold admits a Kahler-Einstein metric and give a brief outline of the proof.

The main purpose of this paper is to prove a general result about the geometry of holomorphic line bundles over Khler manifolds. This result is essentially a partial verification of a conjecture of Tian [30] and Tian has, over many years, highlighted the importance of the question for the existence theory of KhlerEinstein metrics. We will begin by stating this main result.

This is the first of a series of three papers which prove the fact that a K-stable Fano manifold admits a Khler-Einstein metric. The main result of this paper is that a Khler-Einstein metric with cone singularities along a divisor can be approximated by a sequence of smooth Khler metrics with controlled geometry in the Gromov-Hausdorff sense.

This is the third and final article in a series which prove the fact that a K-stable Fano manifold admits a Khler-Einstein metric. In this paper we consider the Gromov-Hausdorff limits of metrics with cone singularities in the case when the limiting cone angle approaches 2\pi . We also put all our technical results together to complete the proof of the main theorem.

When people in a society want to make inference about some parameter, each person would potentially want to use data collected by other people. Information (data) exchange in social contexts is usually costly, so to make sound statistical decisions, people need to compromise between benefits and costs of information acquisition. Conflicts of interests and coordination will arise. Classical statistics does not consider peoples interaction in the data collection process. To address this ignorance, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Bearing our interest in aggregate inference at the societal level, we propose a new concept finite population learning to address whether with high probability, a large fraction of people can make good inferences. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.