In Bayesian inference, we seek to compute information about random variables such as moments or quantiles on the basis of {available data} and prior information. When distribution is {intractable}, Monte Carlo (MC) sampling usually required. {Importance a standard MC tool that approximates this unavailable with set weighted samples.} This procedure asymptotically consistent number samples (part...

Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner. However previous attempts toward developing online Monte-Carlo methods for LDA have little success, often by having much worse perplexity than their batch counterparts. We present a streaming Gibbs sampling (SGS) method, an online extension of the collapsed Gibbs sampling (CGS). Our empirical study shows...

Aproximating a sum without computing the summands is a classic problem in statistics and machine learning. The problem is defined as follows: Assume Z is the sum of n numbers, Z1, · · · , Zn i.e., Z = Z1 + · · ·+ Zn. The goal is to estimate Z without computing all the n summands but few. According to the uniform sampling we choose a number Zi with probability 1 n and assign the weight n to Zi. ...

We show how rapidly changing textual streams such as Twitter can be modelled in fixed space. Our approach is based upon a randomised algorithm called Exponential Reservoir Sampling, unexplored by this community until now. Using language models over Twitter and Newswire as a testbed, our experimental results based on perplexity support the intuition that recently observed data generally outweigh...

In the era of big data, graph sampling is indispensable in many settings. Existing sampling methods are mostly designed for static graphs, and aim to preserve basic structural properties of the original graph (such as degree distribution, clustering coefficient etc.) in the sample. We argue that for any sampling method it is impossible to produce an universal representative sample which can pre...

Attentive robots, inspired by human-like vision – are required to have visual systems with fovea-periphery distinction and saccadic motion capability. Thus, each frame in the incoming image sequence has nonuniform sampling and consecutive saccadic images have temporal redundancy. In this paper, we propose a novel video coding and streaming algorithm for low bandwidth networks that exploits thes...

To improve the availability of communication bandwidth in distributed systems, communication overhead should be reduced as much as possible. This paper focuses on distributed data-stream systems. In such a network, large number of sensors delivers continuous data to a central server. The sampling rate of each sensor affects the communication resource and the computational load at central server...

We develop a streaming (one-pass, boundedmemory) word embedding algorithm based on the canonical skip-gram with negative sampling algorithm implemented in word2vec. We compare our streaming algorithm to word2vec empirically by measuring the cosine similarity between word pairs under each algorithm and by applying each algorithm in the downstream task of hashtag prediction on a two-month interva...