مقاله انگلیسی رایگان در مورد مدل های انتخاب زنجیره مارکوف – الزویر ۲۰۲۰

مقاله انگلیسی رایگان در مورد مدل های انتخاب زنجیره مارکوف – الزویر ۲۰۲۰

 

مشخصات مقاله
ترجمه عنوان مقاله شناسایی پارامتر در مدل های انتخاب زنجیره مارکوف
عنوان انگلیسی مقاله Parameter identification in Markov chain choice models
انتشار مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی ۹ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۱٫۲۲۰ در سال ۲۰۱۹
شاخص H_index ۱۰۵ در سال ۲۰۲۰
شاخص SJR ۰٫۴۹۴ در سال ۲۰۱۹
شناسه ISSN ۰۳۰۴-۳۹۷۵
شاخص Quartile (چارک) Q2 در سال ۲۰۱۹
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط مهندسی الگوریتم و محاسبات
نوع ارائه مقاله
ژورنال
مجله  علوم نظری رایانه – Theoretical Computer Science
دانشگاه  Princeton University, Princeton, NJ, United States of America
کلمات کلیدی مدلهای انتخاب گسسته، مدل های انتخاب زنجیره مارکوف، شناسایی پارامتر
کلمات کلیدی انگلیسی Discrete choice models، Markov chain choice models، Parameter identification
شناسه دیجیتال – doi
https://doi.org/10.1016/j.tcs.2019.11.014
کد محصول E14535
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
۱٫ Introduction
۲٫ Model and notation
۳٫ Main result
۴٫ Proof of Theorem 1
۵٫ Sample complexity
۶٫ Discussion
Declaration of Competing Interest
Acknowledgements
References

بخشی از متن مقاله:
Abstract

This work studies the parameter identification problem for the Markov chain choice model of Blanchet, Gallego, and Goyal used in assortment planning. In this model, the product selected by a customer is determined by a Markov chain over the products, where the products in the offered assortment are absorbing states. The underlying parameters of the model were previously shown to be identifiable from the choice probabilities for the all-products assortment, together with choice probabilities for assortments of all-but-one products. Obtaining and estimating choice probabilities for such large assortments is not desirable in many settings. The main result of this work is that the parameters may be identified from assortments of sizes two and three, regardless of the total number of products. The result is obtained via a simple and efficient parameter recovery algorithm.

Introduction

In assortment planning, the seller’s goal is to select a subset of products (called an assortment) to offer to a customer so as to maximize the expected revenue. This task can be formulated as an optimization problem given the revenue generated from selling each product, along with a probabilistic model of the customer’s preferences for the products. Such a discrete choice model must capture the customer’s substitution behavior when, for instance, the offered assortment does not contain the customer’s most preferred product. Our focus in this paper is the Markov chain choice model (MCCM) proposed by Blanchet et al. [2]. This MCCM chooses to model the product selected by the customer as a Markov chain over products where the products in the offered assortment are absorbing states. The current state represents the desired product; if that product is not offered, the customer transitions to another product according to the Markov chain probabilities, and the process continues until the desired product is offered or the customer leaves. MCCM generalizes widely-used discrete choice models such as the multinomial logit model [6,8], as well as other generalized attraction models [5]; it also well-approximates other random utility models found in the literature such as mixed multinomial logit models [7]. At the same time, the MCCM permits computationally efficient unconstrained assortment optimization as well as efficient approximation algorithms in the constrained case [2,4]; this stands in contrast to some richer models such as mixed multinomial logit models [9] and the nested logit model [3] for which assortment optimization is generally intractable. This combination of expressiveness and computational tractability makes MCCM very attractive for use in assortment planning. A crucial step in this overall enterprise—e.g., before assortment optimization may take place—is the estimation of the choice model’s parameters from observational data. Parameter estimation for MCCM is only briefly considered in the original work of Blanchet et al. [2].

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