It has been shown previously that a Maximum Entropy model can be used to learn the correct weights on linguistic constraints in the supervised case [4]. In this paper, we show how to extend the existing work to learn underlying forms in addition to linguistic constraints, discussing the practical details of the priors and the constraint sets that allow this to work. In addition, we discuss a te...

The following picture shows the test omission rate and predicted area as a function of the cumulative threshold, averaged over the replicate runs. The omission rate should be close to the predicted omission, because of the definition of the cumulative threshold. The next picture is the receiver operating characteristic (ROC) curve for the same data, again averaged over the replicate runs. Note ...

BACKGROUND Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. METHODOLOGY We collected the dis...

The principle of maximum entropy has recently been applied to several problems of condensed matter theory. In this paper we discuss some technical aspects of the maxent approach to these problems, and show some general properties of the applications of the method. In particular, we show that maxent can be thought of as a convenient way to close hierarchies, and to extrapolate perturbation serie...

Maximum Entropy (MaxEnt) language models are powerful models that can incorporate linguistic and non-linguistic contextual signals in a unified framework with a convex loss. MaxEnt models also have the advantage of scaling to large model and training data sizes We present the following two contributions to MaxEnt training: (1) By leveraging smaller amounts of transcribed data, we demonstrate th...

Maximum entropy inverse optimal control (MaxEnt IOC) is an effective means of discovering the underlying cost function of demonstrated human activity and can be used to predict human behavior over low-dimensional state spaces (i.e., forecasting of 2D trajectories). To enable inference in very large state spaces, we introduce an approximate MaxEnt IOC procedure to address the fundamental computa...

The variational principles called maximum entropy (MaxEnt) and maximum caliber (MaxCal) are reviewed. MaxEnt originated in the statistical physics of Boltzmann and Gibbs, as a theoretical tool for predicting the equilibrium states of thermal systems. Later, entropy maximization was also applied to matters of information, signal transmission, and image reconstruction. Recently, since the work of...

MaxEnt grammar is a probabilistic version of Harmonic Grammar in which the harmony scores candidates are mapped onto probabilities. It has become tool choice for analyzing phonological phenomena involving variation or gradient acceptability, but there competing proposal making probabilistic, Noisy Grammar, derived by adding random ‘noise’ to constraint weights. In this paper these frameworks, a...

ix CHAPTER 1: INTRODUCTION 1 1.1 Thesis Organization 7 CHAPTER 2: RELATED WORK 8 2.1 Types of Species Distribution Models 8 2.2 The MAXENT Model and Applications 10 CHAPTER 3: METHODS AND DATA 15 3.1 Typical Species Distribution Model Workflow 15 3.2 Presence Data 17 3.3 Explanatory Variables 17 3.4 MAXENT Model Runs 22 3.5 Assessment of Model and Variable Performance 23 CHAPTER 4: RESULTS 25 4...