This paper proposes a method of estimating and simulating a maximum entropy distribution given moment conditions based on MonteCarlo approach. The method provides an simple alternative to conventional calculation methods of maximum entropy densities which involve complex numerical integration and are subject to occasional failure. We first show that maximum entropy density on a random sample co...

The conventional n-gram language model exploits only the immediate context of historical words without exploring long-distance semantic information. In this paper, we present a new information source extracted from latent semantic analysis (LSA) and adopt the maximum entropy (ME) principle to integrate it into an n-gram language model. With the ME approach, each information source serves as a s...

Reconstructing maps of fluctuations in the Cosmic Microwave Background (CMB) radiation from observed data requires a number of steps in the data analysis. Among these are the removal of receiver noise, the deconvolution of the image and the separation and subtraction of foreground contaminants. The latter is usually achieved by the use of multifrequency observations, which exploit the different...

As global temperatures rise due to climate change, crops are becoming increasingly vulnerable to failure. We tested the accessibility of thermodynamic phenotypic diversity within limited positional changes using 2 9 =512 mutants of a terpene synthase (TPS), Tobacco 5-epi-Aristolchene synthase. First, we measured the thermal unfolding curves of each mutant and found that mutations shifted the T ...

Recent research has shown there are two types of uncertainty that can be expressed in first-order logic— propositional and statistical uncertainty—and that both types can be represented in terms of probability spaces. However, these efforts have fallen short of providing a general account of how to design probability measures for these spaces; as a result, we lack a crucial component of any sys...

We consider the “remote-sampling” problem of choosing a subset S, with |S|= s, from a set N of observable random variables, so as to obtain as much information as possible about a set T of target random variables which are not directly observable. Our criterion is that of minimizing the entropy of T conditioned on S. We con ne our attention to the case in which the random variables have a joint...

We study maximum entropy correlated equilibria in (multiplayer) games and provide two gradient-based algorithms that are guaranteed to converge to such equilibria. Although we do not provide convergence rates for these algorithms, they do have strong connections to other algorithms (such as iterative scaling) which are effective heuristics for tasks such as statistical estimation.

The principle of Maximum Entropy (ME) provides a consistent method of inference for estimating the form of an unknown discrete-state probability distribution, based on information expressed in terms of tree expected values. In this tutorial paper entropy maximisation is used to characterise product-form approximations and resolution algorithms for arbitrary continuoustime and discrete-time Queu...

We give an algorithm for calculating the maximum entropy state as the least fixed point of a Scott continuous mapping on the domain of classical states in their Bayesian order.