Online purchasing under uncertainty

Online purchasing under uncertainty

Suppose there is a collection x1, x2, . . . , xN of independent uniform [0, 1] random variables, and a hypergraph F of target structures on the vertex set {1, . . . , N}. We would like to purchase a target structure at small cost, but we do not know all the costs xi ahead of time. Instead, we inspect the random variables xi one at a time, and after each inspection, choose to either keep the ver...

Purchasing a C_4 online

Let G be a graph with edge set (e1, e2, ...eN ). We independently associate to each edge ei of G a cost xi that is drawn from a Uniform [0, 1] distribution. Suppose F is a set of targeted structures that consists of subgraphs of G. We would like to buy a subset of F at small cost, however we do not know a priori the values of the random variables x1, ..., xN . Instead, we inspect the random var...

Predicting online-purchasing behaviour

This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson’s global score search algorithm to find the best subset of predict...

Importance Sampling for Online Planning under Uncertainty

The partially observable Markov decision process (POMDP) provides a principled general framework for robot planning under uncertainty. Leveraging the idea of Monte Carlo sampling, recent POMDP planning algorithms have scaled up to various challenging robotic tasks, including, e.g., real-time online planning for autonomous vehicles. To further improve online planning performance, this paper pres...

Study of China’s online catering market under the booming of online group purchasing