Noise and speaker adaptation techniques are essential to realize robust speech recognition in real noisy environments . In this paper, we applied spectral subtraction to an unsupervised speaker adaptation algorithm in noisy environments. The adaptation algorithm consists of the following five steps. (1) Spectral subtraction is carried out for noise added database. (2) Noise matched acoustic mod...

The problem of generating discrete sufficient statistics for signal processing in code-division multiple-access (CDMA) systems is considered in the context of underlying channel bandwidth restrictions. Discretization schemes are identified for (approximately) band-limited CDMA systems, and a notion of approximate sufficiency is introduced. The role of chip-matched filtering in generating accura...

Conjugate pairs of distributions over infinite dimensional spaces are prominent in machine learning, particularly due to the widespread adoption of Bayesian nonparametric methodologies for a host of models and applications. Much of the existing literature in the learning community focuses on processes possessing some form of computationally tractable conjugacy as is the case for the beta proces...

A sufficient statistic is a significant concept in statistics, which means a probability variable that has sufficient information required for an inference task. We investigate the roles of sufficient statistics and related quantities in stochastic thermodynamics. Specifically, we prove that for general continuous-time bipartite networks, the existence of a sufficient statistic implies that an ...

The debate between “structural” and “reduced-form”approaches has generated substantial controversy in applied economics. This article reviews a recent literature in public economics that combines the advantages of reduced-form strategies –transparent and credible identi…cation –with an important advantage of structural models – the ability to make predictions about counterfactual outcomes and w...

Conjugate pairs of distributions over infinite dimensional spaces are prominent in statistical learning theory, particularly due to the widespread adoption of Bayesian nonparametric methodologies for a host of models and applications. Much of the existing literature in the learning community focuses on processes possessing some form of computationally tractable conjugacy as is the case for the ...

The debate between “structural” and “reduced-form”approaches has generated substantial controversy in applied economics. This article reviews a recent literature in public economics that combines the advantages of reduced-form strategies –transparent and credible identi…cation –with an important advantage of structural models – the ability to make predictions about counterfactual outcomes and w...

In this work, applying Lemma due to Nunokawa et. al. cite{NCKS}, we obtain some sufficient inequalities for some certain subclasses of univalent functions.

Exponential-family random graph models (ERGMs) are probabilistic network models that are parametrized by sufficient statistics based on structural (i.e., graph-theoretic) properties. The ergm package for the R statistical computing system is a collection of tools for the analysis of network data within an ERGM framework. Many different network properties can be employed as sufficient statistics...

Complicated generative models often result in a situation where computing the likelihood of observed data is intractable, while simulating from the conditional density given a parameter value is relatively easy. Approximate Bayesian Computation (ABC) is a paradigm that enables simulation-based posterior inference in such cases by measuring the similarity between simulated and observed data in t...