Language modeling (LM) involves determining the joint probability of words in a sentence. The conditional approach is dominant, representing the joint probability in terms of conditionals. Examples include n-gram LMs and neural network LMs. An alternative approach, called the random field (RF) approach, is used in whole-sentence maximum entropy (WSME) LMs. Although the RF approach has potential...

One challenge of building and maintaining large-scale data management systems is managing data fusion from multiple data sources. Often times, different data sources may represent the same data element in a slightly different way. These differences may represent an error in the data or a disagreement between sources on the correct value that best represents the data point. When the quantity of ...

We present a joint probability model for statistical machine translation, which automatically learns word and phrase equivalents from bilingual corpora. Translations produced with parameters estimated using the joint model are more accurate than translations produced using IBM Model 4. 1 Motivation Most of the noisy-channel-based models used in statistical machine translation (MT) (Brown et al....

Received Nov 21, 2017 Revised Jan 29, 2018 Accepted Feb 17, 2018 For continuous target following under complex scene, an objective following calculation in light of multi-shading joint likelihood investigation model was introduced. The calculation embraced shading histogram to speak to the actual factual trademark with Camshaft standard and completed exploratory research in such angles as multi...

Spatial relationships are crucial to spatial knowledge representation, such as positioning localities. However, minimal attention has been devoted to positioning localities indoors with locality description. Distance and direction relations are generally used when positioning localities, namely, translating descriptive localities into spatially explicit ones. We propose a joint probability func...

There is no closed form analytical equation or quick method to calculate probabilities based only on the entropy of a signal or process. Except in the cases where there are constraints on the state probabilities, one must typically derive the underlying probabilities through search algorithms. These become more computationally expensive as entropies of higher orders are investigated. In this pa...

Golomb coding is a special case of Huffman coding that is optimal for the data with geometric progression distribution probability. In this paper, we will generalize the Golomb coding by joint probability. In image progression, there are many conditions that the probability of a data not only has geometric distribution probability but also depends on another data. (For example, the difference b...

When agents collaborate to perform a control task, it is of interest to characterize the set of joint probability distributions they can achieve on their joint action space when they are passively provided with external common randomness. We give a simple counterexample to a natural conjecture about this class of joint distributions.

In this work we shall give a probabilistic interpretation of possibilistic expected value, variance, covariance and correlation. 1 Probability and possibility In probability theory, the dependency between two random variables can be characterized through their joint probability density function. Namely, if X and Y are two random variables with probability density functions fX(x) and fY (y), res...

We introduce a probability model for gene regulatory networks, based on a system of Chapman-Kolmogorov equations that represent the dynamics of the concentration levels of each agent in the network. This unifying approach includes the representation of excitatory and inhibitory interactions between agents, second-order interactions which allow any two agents to jointly act on other agents, and ...