  The purpose of Inventory Pricing is to bid the right prices to online ad
opportunities, which is crucial for a Demand-Side Platform (DSP) to win
advertising auctions in Real-Time Bidding (RTB). In the planning stage,
advertisers need the forecast of probabilistic models to make bidding
decisions. However, most of the previous works made strong assumptions on the
distribution form of the winning price, which reduced their accuracy and
weakened their ability to make generalizations. Though some works recently
tried to fit the distribution directly, their complex structure lacked
efficiency on online inference. In this paper, we devise a novel loss function,
Neighborhood Likelihood Loss (NLL), collaborating with a proposed framework,
Arbitrary Distribution Modeling (ADM), to predict the winning price
distribution under censorship with no pre-assumption required. We conducted
experiments on two real-world experimental datasets and one large-scale,
non-simulated production dataset in our system. Experiments showed that ADM
outperformed the baselines both on algorithm and business metrics. By replaying
historical data of the production environment, this method was shown to lead to
good yield in our system. Without any pre-assumed specific distribution form,
ADM showed significant advantages in effectiveness and efficiency,
demonstrating its great capability in modeling sophisticated price landscapes.
