Representative origin-destination (OD) demand tables are a crucial part of making many transportation models relevant to practice. However estimating these OD tables is a challenging problem, even more so determining the confidence intervals on these OD estimates. In this work we propose a method to construct estimates and confidence intervals of OD demand tables from link flow data. Our method...

Accurate prediction of promoters is fundamental to understanding gene expression patterns, where confidence estimation is one of the main requirements. Using recently developed transductive confidence machine (TCM) techniques, we developed a new program TSSP-TCM for the prediction of plant promoters that also provides confidence of the prediction. The program was trained on 132 and 104 sequence...

Abstract Dimensionality reduction algorithms are commonly used for reducing the dimension of multi-dimensional data to visualize them on a standard display. Although many dimensionality such as t-distributed Stochastic Neighborhood Embedding aim preserve close neighborhoods in low-dimensional space, they might not accomplish that every sample and eventually produce erroneous representations. In...

Autonomous agents (AA) will increasingly be interacting with us in our daily lives. While we want the benefits attached to AAs, it is essential that their behavior aligned values and norms. Hence, an AA need estimate norms of humans interacts with, which not a straightforward task when solely observing agent's behavior. This paper analyses what extent able simulated human agent (SHA) based on i...

Consider discrete-time linear distributed averaging dynamics, whereby agents in a network start with uncorrelated and unbiased noisy measurements of common underlying parameter (state the world) iteratively update their estimates following non-Bayesian rule. Specifically, let every agent her estimate to convex combination own current those neighbors network. As result this iterative averaging, ...