Sequential Monte Carlo simulation of dynamical models with slowly varying parameters: application to audio

Fong,; Godsill,

doi:10.1109/icassp.2002.1006065

Cited by 3 publications

(1 citation statement)

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“…In marketing, Briesch et al (2002) has made some inroads into the first problem, specifying semiparametric brand-choice models with nonparametric specifications for the systematic and random components of indirect utility (but allowing no unobserved heterogeneity). There has been a significant amount of recent research on the second problem, with the development of several methods for flexibly accommodating unobserved heterogeneity while parametrically specifying the components of the indirect utility: examples include Fox et al (2011) for finite-mixture distributions, Rossi et al (2005) and Braun and Bonfrer (2011) for mixtures of normals specifications using Dirichlet processes, and Fong and Godsill (2002) for finite-mixture specifications of potentially time-varying heterogeneity using particle filters. Other applications include nonparametric controls for selection and endogeneity concerns.…”

Section: Enriching Demand Models With Primary Datamentioning

confidence: 99%

Structural Workshop Paper—Discrete-Choice Models of Consumer Demand in Marketing

Chintagunta

Nair

2011

Marketing Science

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M arketing researchers have used models of consumer demand to forecast future sales, to describe and test theories of behavior, and to measure the response to marketing interventions. The basic framework typically starts from microfoundations of expected utility theory to obtain an econometric system that describes consumers' choices over available options, and to thus characterize product demand. The basic framework has been augmented significantly to account for quantity choices, to accommodate purchases of several products on a single purchase occasion (multiple discreteness and multicategory purchases), and to allow for asymmetric switching between brands across different price tiers. These extensions have enabled researchers to bring the analysis to bear on several related marketing phenomena of interest. This paper has three main objectives. The first objective is to articulate the main goals of demand analysis-forecasting, measurement, and testing-and to highlight several considerations associated with these goals. Our second objective is to describe the main building blocks of individual-level demand models. We discuss approaches built on direct and indirect utility specifications of demand systems, and we review extensions that have appeared in the marketing literature. The third objective is to explore a few emerging directions in demand analysis, including considering demand-side dynamics, combining purchase data with primary information, and using semiparametric and nonparametric approaches. We hope researchers new to this literature will take away a broader perspective on these models and see the potential for new directions in future research.

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Section: Enriching Demand Models With Primary Datamentioning

confidence: 99%

Structural Workshop Paper—Discrete-Choice Models of Consumer Demand in Marketing

Chintagunta

Nair

2011

Marketing Science

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Musical note segmentation employing combined time and frequency analyses

Velikic

Titlebaum

Bocko

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing

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Particle Filters for Infinite (or Large) Dimensional State Spaces-Part 2

Vaswani

2006

2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings

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We propose particle filtering algorithms for tracking on infinite (or large) dimensional state spaces. We consider the general case where state space may not be a vector space, we assume it to be a separable metric space (Polish space). In implementation, any such space is approximated by a finite but large dimensional vector, whose dimension may vary at every time. Monte carlo sampling from a large dimensional system noise distribution is computationally expensive. Also, the number of particles required for accurate particle filtering increases with the number of independent dimensions of the system noise, making particle filtering even more expensive. But as long as the number of independent system noise dimensions is small, even if the total state space dimension is very large, a particle filtering algorithm can be implemented. In most large dim applications, it is fair to assume that "most of the state change" occurs in a small dimensional basis, which may be fixed or slowly time varying (approximated as piecewise constant). We use this assumption to propose efficient PF algorithms. These are analyzed and extended in [1].

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Sequential Monte Carlo simulation of dynamical models with slowly varying parameters: application to audio

Cited by 3 publications

References 0 publications

Structural Workshop Paper—Discrete-Choice Models of Consumer Demand in Marketing

Structural Workshop Paper—Discrete-Choice Models of Consumer Demand in Marketing

Musical note segmentation employing combined time and frequency analyses

Particle Filters for Infinite (or Large) Dimensional State Spaces-Part 2

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