Journal articles: 'Upper Confidence Bound'

1

Francisco-Valencia, Iván, José Raymundo Marcial-Romero, and Rosa María Valdovinos-Rosas. "Upper Confidence Bound o Upper Cofidence Bound Tuned para General Game Playing: Un estudio empírico." Research in Computing Science 147, no. 8 (December 31, 2018): 301–9. http://dx.doi.org/10.13053/rcs-147-8-23.

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Saffidine, Abdallah, Tristan Cazenave, and Jean Méhat. "UCD : Upper confidence bound for rooted directed acyclic graphs." Knowledge-Based Systems 34 (October 2012): 26–33. http://dx.doi.org/10.1016/j.knosys.2011.11.014.

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Bollabás, Béla, and Alan Stacey. "Approximate upper bounds for the critical probability of oriented percolation in two dimensions based on rapidly mixing Markov chains." Journal of Applied Probability 34, no. 4 (December 1997): 859–67. http://dx.doi.org/10.2307/3215002.

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We develop a technique for establishing statistical tests with precise confidence levels for upper bounds on the critical probability in oriented percolation. We use it to givepc< 0.647 with a 99.999967% confidence. As Monte Carlo simulations suggest thatpc≈ 0.6445, this bound is fairly tight.

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Bollabás, Béla, and Alan Stacey. "Approximate upper bounds for the critical probability of oriented percolation in two dimensions based on rapidly mixing Markov chains." Journal of Applied Probability 34, no. 04 (December 1997): 859–67. http://dx.doi.org/10.1017/s0021900200101573.

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We develop a technique for establishing statistical tests with precise confidence levels for upper bounds on the critical probability in oriented percolation. We use it to give pc < 0.647 with a 99.999967% confidence. As Monte Carlo simulations suggest that pc ≈ 0.6445, this bound is fairly tight.

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Cruse, Thomas A., and Jeffrey M. Brown. "Confidence Interval Simulation for Systems of Random Variables." Journal of Engineering for Gas Turbines and Power 129, no. 3 (October 11, 2005): 836–42. http://dx.doi.org/10.1115/1.2718217.

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Bayesian network models are seen as important tools in probabilistic design assessment for complex systems. Such network models for system reliability analysis provide a single probability of failure value whether the experimental data used to model the random variables in the problem are perfectly known or derive from limited experimental data. The values of the probability of failure for each of those two cases are not the same, of course, but the point is that there is no way to derive a Bayesian type of confidence interval from such reliability network models. Bayesian confidence (or belief) intervals for a probability of failure are needed for complex system problems in order to extract information on which random variables are dominant, not just for the expected probability of failure but also for some upper bound, such as for a 95% confidence upper bound. We believe that such confidence bounds on the probability of failure will be needed for certifying turbine engine components and systems based on probabilistic design methods. This paper reports on a proposed use of a two-step Bayesian network modeling strategy that provides a full cumulative distribution function for the probability of failure, conditioned by the experimental evidence for the selected random variables. The example is based on a hypothetical high-cycle fatigue design problem for a transport aircraft engine application.

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Ottens, Brammert, Christos Dimitrakakis, and Boi Faltings. "DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 528–34. http://dx.doi.org/10.1609/aaai.v26i1.8129.

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The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good solutions to the problem of Go. This paper introduces DUCT, a distributed algorithm inspired by UCT, for solving Distributed Constraint Optimization Problems (DCOP). Bounds on the solution quality are provided, and experiments show that, compared to existing DCOP approaches, DUCT is able to solve very large problems much more efficiently, or to find significantly higher quality solutions.

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Radović, Nevena, and Milena Erceg. "Hardware implementation of the upper confidence-bound algorithm for reinforcement learning." Computers & Electrical Engineering 96 (December 2021): 107537. http://dx.doi.org/10.1016/j.compeleceng.2021.107537.

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Melesko, Jaroslav, and Vitalij Novickij. "Computer Adaptive Testing Using Upper-Confidence Bound Algorithm for Formative Assessment." Applied Sciences 9, no. 20 (October 14, 2019): 4303. http://dx.doi.org/10.3390/app9204303.

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There is strong support for formative assessment inclusion in learning processes, with the main emphasis on corrective feedback for students. However, traditional testing and Computer Adaptive Testing can be problematic to implement in the classroom. Paper based tests are logistically inconvenient and are hard to personalize, and thus must be longer to accurately assess every student in the classroom. Computer Adaptive Testing can mitigate these problems by making use of Multi-Dimensional Item Response Theory at cost of introducing several new problems, most problematic of which are the greater test creation complexity, because of the necessity of question pool calibration, and the debatable premise that different questions measure one common latent trait. In this paper a new approach of modelling formative assessment as a Multi-Armed bandit problem is proposed and solved using Upper-Confidence Bound algorithm. The method in combination with e-learning paradigm has the potential to mitigate such problems as question item calibration and lengthy tests, while providing accurate formative assessment feedback for students. A number of simulation and empirical data experiments (with 104 students) are carried out to explore and measure the potential of this application with positive results.

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Dzhoha, Andrii, and Iryna Rozora. "Beta Upper Confidence Bound Policy for the Design of Clinical Trials." Austrian Journal of Statistics 52, SI (August 15, 2023): 26–39. http://dx.doi.org/10.17713/ajs.v52isi.1751.

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The multi-armed bandit problem is a classic example of the exploration-exploitation trade-off well suited to model sequential resource allocation under uncertainty. One of its typical motivating applications is the adaptive designs in clinical trials which modify the trial's course in accordance with the pre-specified objective by utilizing results accumulating in the trial. Since the response to a procedure in clinical trials is not immediate, the multi-armed bandit policies require adaptation to delays to retain their theoretical guarantees. In this work, we show the importance of such adaptation by evaluating policies using the publicly available datasetThe International Stroke Trial of a randomized trial of aspirin and subcutaneous heparin among 19,435 patients with acute ischaemic stroke. In addition to adapted policies, we analyze the Upper Confidence Bound policy with the beta feedback to mitigate delays when the certainty evidence of successful treatment is available in a relatively short-term period after the procedure.

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WALLINGA, J., D. LÉVY-BRUHL, N. J. GAY, and C. H. WACHMANN. "Estimation of measles reproduction ratios and prospects for elimination of measles by vaccination in some Western European countries." Epidemiology and Infection 127, no. 2 (October 2001): 281–95. http://dx.doi.org/10.1017/s095026880100601x.

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The objective of this study is to estimate the measles reproduction ratio for eight Western European vaccination programmes. Because many plausible age-structured transmission patterns result in a similar description of the observations, it is not possible to estimate a unique value of the reproduction ratio. A method is developed to estimate bounds and confidence intervals for plausible values of the reproduction ratios using maximum likelihood methods. Lower and upper bounds for plausible values of the basic reproduction ratio are estimated to be 7·17 (95% CI 7·14–7·20) and 45·41 (95% CI 9·77–49·57), corresponding to lower and upper bounds on critical vaccine coverage of 86·6% and 98·1%. Of the eight evaluated vaccination programmes, four have vaccine coverage below the lower bound and allow measles to persist, and four have vaccine coverage at the upper bound and may eventually eliminate measles.

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Jin, Rong, David Simchi-Levi, Li Wang, Xinshang Wang, and Sen Yang. "Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses." Management Science 67, no. 8 (August 2021): 4756–71. http://dx.doi.org/10.1287/mnsc.2020.3773.

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The recent rising popularity of ultrafast delivery services on retail platforms fuels the increasing use of urban warehouses, whose proximity to customers makes fast deliveries viable. The space limit in urban warehouses poses a problem for such online retailers: the number of stock keeping units (SKUs) they carry is no longer “the more, the better,” yet it can still be significantly large, reaching hundreds or thousands in a product category. In this paper, we study algorithms for dynamically selecting a large number of products (i.e., SKUs) with top customer purchase probabilities on the fly, from an ocean of potential products to offer on retailers’ ultrafast delivery platforms. We distill the product selection problem into a semibandit model with linear generalization. There are in total N arms corresponding to N products, each with a feature vector of dimension d. The player pulls K arms in each period and observes the bandit feedback from each of the pulled arms. We focus on the setting where K is much greater than the number of total time periods T or the dimension of product features d. We first analyze a standard Upper Confidence Bound (UCB) algorithm and show its regret bound can be expressed as the sum of a T-independent part and a T-dependent part, which we refer to as “fixed cost” and “variable cost,” respectively. To reduce the fixed cost for large K values, we propose a novel online learning algorithm, which iteratively shrinks the upper confidence bounds within each period, and show its fixed cost is reduced by a factor of d. Moreover, we test the algorithms on an industrial data set from Alibaba Group. Experimental results show that our new algorithm reduces the total regret of the standard UCB algorithm by at least 10%. This paper was accepted by J. George Shanthikumar, big data analytics.

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Zeng, Yijia. "Involvement of the variance in the UCB algorithm regarding risk aversion and the regret bound." Applied and Computational Engineering 18, no. 1 (October 23, 2023): 208–14. http://dx.doi.org/10.54254/2755-2721/18/20230992.

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The classical Upper Confidence Bound (UCB) algorithm implemented overestimates of the true mean of reward distributions based on the sample mean and the number of times such arms were chosen to decide the best arm. In this case, the variances, the essential components of any distributions, of reward distributions are dismissed. Moreover, in real-world applications, arms with relatively high means and small variances sometimes are preferable to arms with the highest mean and large variance. Such concerns are considered risk-aversion in the Multi-Armed Bandits algorithm. Additionally, since the combination of the variance and mean could help estimate the range of the distribution, proper utilization of the sample variance might help people to construct a tighter upper confidence bound to perform the UCB algorithm, leading to a smaller regret. In this paper, the author investigates and summarizes the current algorithm regarding risk aversion based on the estimations and implementations of variances in constructing the upper confidence bound in the UCB algorithm.

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13

Solow, Andrew R. "Tests and confidence intervals for a common upper endpoint in fossil taxa." Paleobiology 22, no. 3 (1996): 406–10. http://dx.doi.org/10.1017/s0094837300016353.

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Statistical inference about the upper and lower endpoints of the stratigraphic ranges of fossil taxa can be based on the pattern of finds. Strauss and Sadler (1989) described a test and confidence interval for a common upper or lower endpoint in two or more taxa. This approach is conservative, in the sense that it provides only an upper bound on the significance level. This paper describes and illustrates a test and confidence interval for which the significance level is known.

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14

Satten, Glen A. "Upper and Lower Bound Distributions That Give Simultaneous Confidence Intervals for Quantiles." Journal of the American Statistical Association 90, no. 430 (June 1995): 747–52. http://dx.doi.org/10.1080/01621459.1995.10476568.

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15

Liu, Yizhi. "An investigation of progress related to stochastic stationary bandit algorithms." Applied and Computational Engineering 34, no. 1 (January 22, 2024): 197–201. http://dx.doi.org/10.54254/2755-2721/34/20230326.

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The Multi-armed Bandit algorithm stands as a consequential tool for informed decision-making, distinct from reliance on intuitive selections, given its systematic proclivity to meticulously assess accessible alternatives with the intent of discerning the most auspicious outcome. Amid the repertoire of algorithmic variations, the Stochastic Stationary Bandit algorithm assumes a foundational and enduring role, finding versatile application across diverse domains, including but not limited to digital advertising, price optimization, and recommendation systems. With these considerations in view, the present study embarks upon a comprehensive scrutiny of this subject matter. This paper reviews on the Explore-Then-Commit algorithm, Upper Confidence Bound algorithm, and Thompson Sampling algorithm by explaining, comparing their formulation, features, and expected results. Explore-Then-Commit algorithm has distinct phase to explore all the choices uniformly. Upper Confidence Bound algorithm make decisions by calculate an upper confidence index which is an overestimate for each choice. Thompson Sampling algorithm depends on randomness to make choices. Explore-Then-Commit algorithm faces the problem of when to explore and when to stop. Upper Confidence Bound algorithm and Thompson Sampling algorithm solve this problem by avoid certain phases. Multi-armed Bandit algorithm could deal with the process of displaying items of potential interest to users in a recommendation system, the delivery of resources in resource allocation, or the way to maximize revenue in a business.

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16

Mandel, David R., Robert N. Collins, Evan F. Risko, and Jonathan A. Fugelsang. "Effect of confidence interval construction on judgment accuracy." Judgment and Decision Making 15, no. 5 (September 2020): 783–97. http://dx.doi.org/10.1017/s1930297500007920.

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AbstractThree experiments (N = 550) examined the effect of an interval construction elicitation method used in several expert elicitation studies on judgment accuracy. Participants made judgments about topics that were either searchable or unsearchable online using one of two order variations of the interval construction procedure. One group of participants provided their best judgment (one step) prior to constructing an interval (i.e., lower bound, upper bound, and a confidence rating that the correct value fell in the range provided), whereas another group of participants provided their best judgment last, after the three-step confidence interval was constructed. The overall effect of this elicitation method was not significant in 8 out of 9 univariate tests. Moreover, the calibration of confidence intervals was not affected by elicitation order. The findings warrant skepticism regarding the benefit of prior confidence interval construction for improving judgment accuracy.

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Hauser, Raphael, Servet Martínez, and Heinrich Matzinger. "Large deviations-based upper bounds on the expected relative length of longest common subsequences." Advances in Applied Probability 38, no. 3 (September 2006): 827–52. http://dx.doi.org/10.1239/aap/1158685004.

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Consider the random variable Ln defined as the length of a longest common subsequence of two random strings of length n and whose random characters are independent and identically distributed over a finite alphabet. Chvátal and Sankoff showed that the limit γ=limn→∞E[Ln]/n is well defined. The exact value of this constant is not known, but various methods for the computation of upper and lower bounds have been discussed in the literature. Even so, high-precision bounds are hard to come by. In this paper we discuss how large deviation theory can be used to derive a consistent sequence of upper bounds, (qm)m∈ℕ, on γ, and how Monte Carlo simulation can be used in theory to compute estimates, q̂m, of the qm such that, for given Ξ > 0 and Λ ∈ (0,1), we have P[γ < q̂ < γ + Ξ] ≥ Λ. In other words, with high probability the result is an upper bound that approximates γ to high precision. We establish O((1 − Λ)−1Ξ−(4+ε)) as a theoretical upper bound on the complexity of computing q̂m to the given level of accuracy and confidence. Finally, we discuss a practical heuristic based on our theoretical approach and discuss its empirical behavior.

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Alshahrani, Mohammed, Fuxi Zhu, Soufiana Mekouar, Mohammed Yahya Alghamdi, and Shichao Liu. "Identification of Top-K Influencers Based on Upper Confidence Bound and Local Structure." Big Data Research 25 (July 2021): 100208. http://dx.doi.org/10.1016/j.bdr.2021.100208.

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Pritikin, Joshua N., Lance M. Rappaport, and Michael C. Neale. "Likelihood-Based Confidence Intervals for a Parameter With an Upper or Lower Bound." Structural Equation Modeling: A Multidisciplinary Journal 24, no. 3 (January 27, 2017): 395–401. http://dx.doi.org/10.1080/10705511.2016.1275969.

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Chen, Zu-Cheng, Yu-Mei Wu, and Qing-Guo Huang. "Search for the Gravitational-wave Background from Cosmic Strings with the Parkes Pulsar Timing Array Second Data Release." Astrophysical Journal 936, no. 1 (August 26, 2022): 20. http://dx.doi.org/10.3847/1538-4357/ac86cb.

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Abstract We perform a direct search for an isotropic stochastic gravitational-wave background (SGWB) produced by cosmic strings in the Parkes Pulsar Timing Array (PPTA) Data Release 2 (DR2). We find no evidence for such an SGWB, and therefore place a 95% confidence level upper limit on the cosmic string tension, G μ, as a function of the reconnection probability, p, which can be less than 1 in the string-theory-inspired models or pure Yang–Mills theory. The upper bound on the cosmic string tension is G μ ≲ 5.1 × 10−10 for p = 1, which is about five orders of magnitude tighter than the bound derived from the null search of individual gravitational-wave bursts from cosmic string cusps in the PPTA DR2, and comparable to previous bounds derived from the null search of the SGWB from cosmic strings.

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O'Hara, Anthony J., Bruce H. Faaland, and B. Bruce Bare. "Spatially constrained timber harvest scheduling." Canadian Journal of Forest Research 19, no. 6 (June 1, 1989): 715–24. http://dx.doi.org/10.1139/x89-111.

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Multiple-use management of forests often requires imposition of spatial constraints on the selection of units for harvest. To satisfy such constraints, harvest units must be treated as integral units. A biased sampling search technique is used to find integer solutions to operationally sized problems. Solutions found for the sample problems are within 8% of the upper bound of the corresponding linear programming solution and less than 4% below the upper bound on the true optimum as defined by a confidence interval estimator.

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Lumban Gaol, Irvan, and Charles O. P. Marpaung. "Risk Assessment of Power Generated from a Wind Turbine in Different Climate Cities in Indonesia." International Journal of Smart Grid and Sustainable Energy Technologies 2, no. 1 (December 18, 2019): 97–102. http://dx.doi.org/10.36040/ijsgset.v2i1.223.

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In this paper, a risk analysis based on Monte Carlo Simulation has been used to examine the power generated from a wind turbine. There are five cities are selected based on the wind speed to be examined the power density generated from the wind turbine. The cities are Kupang, Tanjung Pinang, Krinci, Kotabaru, and Pontianak. Among the five cities, Kupang has the highest wind speed, while Pontianak has the lowest wind speed. In this study, the wind speed is assumed to be an unspecified parameter or a random variable. The Monte Carlo Simulation is run by using a software @RISK. The results show that the mean of power density generated from the wind turbine is found 171.23, 113.97, 71.28, 28.67, and 12.49 W/m2 for Kupang, Tanjung Pinang, Krinci, Kotabaru, and Pontianak respectively. The width of the confidence interval with the level of probability 90% is 110.30, 75.00, 69.10, 19.64, and 7.34 W/m2 for Kupang, Tanjung Pinang, Krinci, Kotabaru, and Pontianak respectively. The upper bound of the confidence intervals are 230.1, 154.7, 113.3, 39.27, and 16.30 W/m2 for Kupang, Tanjung Pinang, Krinci, Kotabaru, and Pontianak respectively, while the lower bounds are 119.8, 79.7, 44.2, and 19.63 W/m2 for Kupang, Tanjung Pinang, Krinci, Kotabaru, and Pontianak respectively. The probability of the power density will exceed the upper bound or will below the lower bound is 5%.

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Hauser, Raphael, Servet Martínez, and Heinrich Matzinger. "Large deviations-based upper bounds on the expected relative length of longest common subsequences." Advances in Applied Probability 38, no. 03 (September 2006): 827–52. http://dx.doi.org/10.1017/s0001867800001294.

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Consider the random variable L n defined as the length of a longest common subsequence of two random strings of length n and whose random characters are independent and identically distributed over a finite alphabet. Chvátal and Sankoff showed that the limit γ=lim n→∞E[L n ]/n is well defined. The exact value of this constant is not known, but various methods for the computation of upper and lower bounds have been discussed in the literature. Even so, high-precision bounds are hard to come by. In this paper we discuss how large deviation theory can be used to derive a consistent sequence of upper bounds, (q m ) m∈ℕ, on γ, and how Monte Carlo simulation can be used in theory to compute estimates, q̂ m , of the q m such that, for given Ξ > 0 and Λ ∈ (0,1), we have P[γ < q̂ < γ + Ξ] ≥ Λ. In other words, with high probability the result is an upper bound that approximates γ to high precision. We establish O((1 − Λ)−1Ξ−(4+ε)) as a theoretical upper bound on the complexity of computing q̂ m to the given level of accuracy and confidence. Finally, we discuss a practical heuristic based on our theoretical approach and discuss its empirical behavior.

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Zhang, Yu, Peixiang Cai, Changyong Pan, and Subing Zhang. "Multi-Agent Deep Reinforcement Learning-Based Cooperative Spectrum Sensing With Upper Confidence Bound Exploration." IEEE Access 7 (2019): 118898–906. http://dx.doi.org/10.1109/access.2019.2937108.

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Liang, Yuan, Chunlin Huang, Xiuguo Bao, and Ke Xu. "Sequential dynamic event recommendation in event-based social networks: An upper confidence bound approach." Information Sciences 542 (January 2021): 1–23. http://dx.doi.org/10.1016/j.ins.2020.06.047.

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Schennach, Susanne M. "A Bias Bound Approach to Non-parametric Inference." Review of Economic Studies 87, no. 5 (January 10, 2020): 2439–72. http://dx.doi.org/10.1093/restud/rdz065.

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Abstract The traditional approach to obtain valid confidence intervals for non-parametric quantities is to select a smoothing parameter such that the bias of the estimator is negligible relative to its standard deviation. While this approach is apparently simple, it has two drawbacks: first, the question of optimal bandwidth selection is no longer well-defined, as it is not clear what ratio of bias to standard deviation should be considered negligible. Second, since the bandwidth choice necessarily deviates from the optimal (mean squares-minimizing) bandwidth, such a confidence interval is very inefficient. To address these issues, we construct valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. The key difficulty in achieving this involves finding a strict, yet feasible, bound on the bias of a non-parametric estimator. It is well-known that it is not possible to consistently estimate the pointwise bias of an optimal non-parametric estimator (for otherwise, one could subtract it and obtain a faster convergence rate violating Stone’s bounds on the optimal convergence rates). Nevertheless, we find that, under minimal primitive assumptions, it is possible to consistently estimate an upper bound on the magnitude of the bias, which is sufficient to deliver a valid confidence interval whose length decreases at the optimal rate and which does not contradict Stone’s results.

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Dai, Yue, Jiangang Lu, Zhiwen Yu, and Ruifeng Zhao. "High-Precision Timing Method of BeiDou-3 System Based on Reinforcement Learning." Journal of Physics: Conference Series 2401, no. 1 (December 1, 2022): 012093. http://dx.doi.org/10.1088/1742-6596/2401/1/012093.

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Abstract All aspects of smart grid operation require precise timing technology to improve the timeliness and precision of the network. However, due to the complex and time-varying network environment and the low precision of device timing, the existing timing technology cannot meet the timing precision required by smart grid services. Therefore, according to the multi-armed bandit (MAB) theory and the upper confidence bound (UCB) algorithm, this paper proposes a high-precision timing method for power transmission and distribution park named adaptive timing-aware upper confidence bound (AUCB). The long-term average reward is maximized based on local information and historical decision-making experience. The simulation results show that the proposed high-precision timing method of AUCB in the power transmission and distribution park can effectively improve the device timing precision and realize self-adaptive timing for power system devices.

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Zhang, Xiaoning. "The investigation and application of Muti-Armed algorithms in recommendation systems." Applied and Computational Engineering 34, no. 1 (January 22, 2024): 260–64. http://dx.doi.org/10.54254/2755-2721/34/20230346.

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In the contemporary Internet recommendation systems in various fields, muti-armed algorithms will show high accuracy and practicability. Internet users can always get abundant positive feedback through the recommendation system via utilizing these algorithms. In this paper, there are three most typical muti-armed algorithms as examples are provided. Explore-Then-Commit (ETC) algorithm is the first to mentioned. The physical meaning of this algorithm is that in the exploration stage, the action will be selected in a certain order, and after a certain number of rounds, the action with the largest average reward will be directly selected. Moreover, Upper Confidence Bond (UCB) algorithm is also a type of pivotal tool. The main function of UCB algorithm is to make the selection by calculating the upper bound of each arm confidence interval instead of the expected reward of the slot machine. Thus, it is an optimistic algorithm. First, select each arm in random order, then calculate the value of the upper bound of each arm confidence interval, and finally, select the arm with the largest value. The last mentioned Thompson Sampling (TS) Algorithm take Bayesian optimization as the theoretical basis. This algorithm takes out the candidate parameters, generates a random number, and selects the maximum value for input. This paper also introduced two fields related to the topic of the application of muti-armed algorithms in the recommendation systems in modern fields. News recommendation algorithms and personalized recommendation systems can be more comprehensive and representative to illustrate the practicality of these algorithms. Therefore, muti-armed algorithms reflect the importance in the recommendation fields via the balance of exploration and exploitation.

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Levenback, Charles F., Shamshad Ali, Robert L. Coleman, Michael A. Gold, Jeffrey M. Fowler, Patricia L. Judson, Maria C. Bell, et al. "Lymphatic Mapping and Sentinel Lymph Node Biopsy in Women With Squamous Cell Carcinoma of the Vulva: A Gynecologic Oncology Group Study." Journal of Clinical Oncology 30, no. 31 (November 1, 2012): 3786–91. http://dx.doi.org/10.1200/jco.2011.41.2528.

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Purpose To determine the safety of sentinel lymph node biopsy as a replacement for inguinal femoral lymphadenectomy in selected women with vulvar cancer. Patients and Methods Eligible women had squamous cell carcinoma, at least 1-mm invasion, and tumor size ≥ 2 cm and ≤ 6 cm. The primary tumor was limited to the vulva, and there were no groin lymph nodes that were clinically suggestive of cancer. All women underwent intraoperative lymphatic mapping, sentinel lymph node biopsy, and inguinal femoral lymphadenectomy. Histologic ultra staging of the sentinel lymph node was prescribed. Results In all, 452 women underwent the planned procedures, and 418 had at least one sentinel lymph node identified. There were 132 node-positive women, including 11 (8.3%) with false-negative nodes. Twenty-three percent of the true-positive patients were detected by immunohistochemical analysis of the sentinel lymph node. The sensitivity was 91.7% (90% lower confidence bound, 86.7%) and the false-negative predictive value (1-negative predictive value) was 3.7% (90% upper confidence bound, 6.1%). In women with tumor less than 4 cm, the false-negative predictive value was 2.0% (90% upper confidence bound, 4.5%). Conclusion Sentinel lymph node biopsy is a reasonable alternative to inguinal femoral lymphadenectomy in selected women with squamous cell carcinoma of the vulva.

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Kolokoltsov, Vassili, Feng Lin, and Aleksandar Mijatović. "Monte carlo estimation of the solution of fractional partial differential equations." Fractional Calculus and Applied Analysis 24, no. 1 (January 29, 2021): 278–306. http://dx.doi.org/10.1515/fca-2021-0012.

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Abstract The paper is devoted to the numerical solutions of fractional PDEs based on its probabilistic interpretation, that is, we construct approximate solutions via certain Monte Carlo simulations. The main results represent the upper bound of errors between the exact solution and the Monte Carlo approximation, the estimate of the fluctuation via the appropriate central limit theorem (CLT) and the construction of confidence intervals. Moreover, we provide rates of convergence in the CLT via Berry-Esseen type bounds. Concrete numerical computations and illustrations are included.

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Zhou, Zeheng, Ying Jiang, Weifeng Liu, Ruifan Wu, Zerong Li, and Wenchao Guan. "A Fast Algorithm for Estimating Two-Dimensional Sample Entropy Based on an Upper Confidence Bound and Monte Carlo Sampling." Entropy 26, no. 2 (February 10, 2024): 155. http://dx.doi.org/10.3390/e26020155.

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The two-dimensional sample entropy marks a significant advance in evaluating the regularity and predictability of images in the information domain. Unlike the direct computation of sample entropy, which incurs a time complexity of O(N2) for the series with N length, the Monte Carlo-based algorithm for computing one-dimensional sample entropy (MCSampEn) markedly reduces computational costs by minimizing the dependence on N. This paper extends MCSampEn to two dimensions, referred to as MCSampEn2D. This new approach substantially accelerates the estimation of two-dimensional sample entropy, outperforming the direct method by more than a thousand fold. Despite these advancements, MCSampEn2D encounters challenges with significant errors and slow convergence rates. To counter these issues, we have incorporated an upper confidence bound (UCB) strategy in MCSampEn2D. This strategy involves assigning varied upper confidence bounds in each Monte Carlo experiment iteration to enhance the algorithm’s speed and accuracy. Our evaluation of this enhanced approach, dubbed UCBMCSampEn2D, involved the use of medical and natural image data sets. The experiments demonstrate that UCBMCSampEn2D achieves a 40% reduction in computational time compared to MCSampEn2D. Furthermore, the errors with UCBMCSampEn2D are only 30% of those observed in MCSampEn2D, highlighting its improved accuracy and efficiency.

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Smith, Malcolm A., Lawrence Rubinstein, James R. Anderson, Diane Arthur, Paul J. Catalano, Boris Freidlin, Ruth Heyn, et al. "Secondary Leukemia or Myelodysplastic Syndrome After Treatment With Epipodophyllotoxins." Journal of Clinical Oncology 17, no. 2 (February 1999): 569. http://dx.doi.org/10.1200/jco.1999.17.2.569.

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PURPOSE: The incidence of secondary leukemia after epipodophyllotoxin treatment and the relationship between epipodophyllotoxin cumulative dose and risk are not well characterized. The Cancer Therapy Evaluation Program (CTEP) of the National Cancer Institute (NCI) has developed a monitoring plan to obtain reliable estimates of the risk of secondary leukemia after epipodophyllotoxin treatment. METHODS: Twelve NCI-supported cooperative group clinical trials were identified that use epipodophyllotoxins at low (< 1.5 g/m2 etoposide), moderate (1.5 to 2.99 g/m2 etoposide), or higher (≥ 3.0 g/m2 etoposide) cumulative doses. Cases of secondary leukemia (including treatment-related myelodysplastic syndrome) occurring on these trials have been reported to CTEP, as has duration of follow-up for all patients, thereby allowing calculation of cumulative 6-year incidence rates of secondary leukemia for each etoposide dose group. RESULTS: The calculated cumulative 6-year risks for development of secondary leukemia for the low, moderate, and higher cumulative dose groups were 3.3%, (95% upper confidence bound of 5.9%), 0.7% (95% upper confidence bound of 1.6%), and 2.2%, (95% upper confidence bound of 4.6%), respectively. CONCLUSION: Within the context of the epipodophyllotoxin cumulative dose range and schedules of administration encompassed by the monitoring plan regimens, and within the context of multiagent chemotherapy regimens that include alkylating agents, doxorubicin, and other agents, factors other than epipodophyllotoxin cumulative dose seem to be of primary importance in determining the risk of secondary leukemia. Data obtained by the CTEP secondary leukemia monitoring plan support the relative safety of using epipodophyllotoxins according to the therapeutic plans outlined in the monitored protocols.

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Tasuab, Irna Ireni, Santosa Santosa, and Ahmad Tabrani. "Pengaruh Mentoring terhadap Identitas Diri Remaja Peserta Program Future Center Yayasan PESAT Cluster Kalimantan Barat." TELEIOS: Jurnal Teologi dan Pendidikan Agama Kristen 3, no. 2 (December 30, 2023): 213–26. http://dx.doi.org/10.53674/teleios.v3i2.66.

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Abstract: Mentoring is a process of trusting relationships in which the process of guidance, emotional support, goal achievement support, training, and teaching are included. This study aims to describe: adolescent self-identity, the mentoring process, and how strong the influence of mentoring on the development of adolescent self-identity. The study was conducted on 52 adolescents in the category of junior high school students participating in the Future Center program of the PESAT Foundation in the West Kalimantan cluster. The research method used is the quantitative research method. Data collection using questionnaires. The results of the first hypothesis research obtained a Lower Bound Interval confidence value of 58.32 and an Upper Bound Interval confidence value of 63.38, or the influence of mentoring in the strong category. The results of the second hypothesis test obtained a Lower Bound Interval confidence value of 40.27 and an Upper Bound Interval confidence value of 43.38, or it can be concluded that the self-identity of adolescents in the category is sufficient. The third hypothesis test obtained an r-value of 0.664 or in the strong category. Based on the results of the correlation coefficient, mentoring has a strong effect on adolescent self-identity. In the Model Summary table for R square (R²) of 0.441, which is then in percentage to 44.1%. This means that the percentage of the contribution of mentoring influence on the self-identity of adolescents participating in the Future Center program of the PESAT Foundation West Kalimantan cluster in the category of junior high school students is 44.1% and 55.9% is influenced by other variables. Mentoring has an important role in building adolescent identity. Therefore, it is necessary to equip mentors with knowledge and skills to assist youth participants in the Future Center program in West Kalimantan.Abstrak: Mentoring merupakan proses hubungan saling percaya yang di dalamnya termuat proses pembimbingan, pemberian dukungan emosional, dukungan pencapaian tujuan, pelatihan dan pengajaran. Penelitian ini bertujuan mendeskripsikan: identitas diri remaja, proses mentoring dan seberapa kuat pengaruh mentoring terhadap perkembangan identitas diri remaja. Penelitian dilakukan pada 52 remaja kategori pelajar SMP peserta program Future Center Yayasan PESAT di cluster Kalimantan Barat. Penelitian ini menggunakan metode kuantitatif. Pengumpulan data menggunakan kuesioner. Hasil penelitian hipotesis pertama diperoleh nilai convidedance Interval Lower Bound sebesar 58,32 dan nilai convidedance Interval Upper Bound sebesar 63,38, atau pengaruh mentoring pada kategori kuat. Hasil uji hipotesis kedua, diperoleh nilai convidedance Interval Lower Bound sebesar 40,27 dan nilai convidedance Interval Upper Bound sebesar 43,38, atau dapat disimpulkan bahwa identitas diri remaja pada kategori cukup. Uji hipotesis ketiga diperoleh nilai r sebesar 0,664, atau pada kategori kuat. Berdasarkan hasil koefisien korelasi maka mentoring berpengaruh kuat terhadap identitas diri remaja. Sedangkan pada tabel Model Summary untuk R square (R²) sebesar 0,441, yang kemudian di presentase menjadi 44,1%. Artinya bahwa presentase sumbangan pengaruh mentoring terhadap identitas diri remaja peserta program Future Center Yayasan PESAT cluster Kalimantan Barat kategori pelajar SMP sebesar 44,1% dan 55,9% dipengaruhi oleh variabel lain. Mentoring memiliki peran penting dalam membangun identas remaja. Oleh sebab itu, perlu memperlengkapi para mentor dengan pengetahuan dan keterampilan dalam mendampingi remaja peserta prgram Future Center di Kalimantan Barat

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Becker, Sebastian, Patrick Cheridito, and Arnulf Jentzen. "Pricing and Hedging American-Style Options with Deep Learning." Journal of Risk and Financial Management 13, no. 7 (July 19, 2020): 158. http://dx.doi.org/10.3390/jrfm13070158.

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In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate dynamic hedging strategy. We test the approach on different specifications of a Bermudan max-call option. In all cases it produces highly accurate prices and dynamic hedging strategies with small replication errors.

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Morris, Stephen, and Hyun Song Shin. "Contagious Adverse Selection." American Economic Journal: Macroeconomics 4, no. 1 (January 1, 2012): 1–21. http://dx.doi.org/10.1257/mac.4.1.1.

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We illustrate the corrosive effect of even small amounts of adverse selection in an asset market and show how it can lead to the total breakdown of trade. The problem is the failure of “market confidence,” defined as approximate common knowledge of an upper bound on expected losses. Small probability events can unravel market confidence. We discuss the role of contagious adverse selection and the problem of “toxic assets” in the recent financial crisis. (JEL D82, G01, G12, G14)

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Zhai, Yaoguang, Zhizhen Qin, and Sicun Gao. "Sample-and-Bound for Non-convex Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (March 24, 2024): 20847–55. http://dx.doi.org/10.1609/aaai.v38i18.30074.

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Standard approaches for global optimization of non-convex functions, such as branch-and-bound, maintain partition trees to systematically prune the domain. The tree size grows exponentially in the number of dimensions. We propose new sampling-based methods for non-convex optimization that adapts Monte Carlo Tree Search (MCTS) to improve efficiency. Instead of the standard use of visitation count in Upper Confidence Bounds, we utilize numerical overapproximations of the objective as an uncertainty metric, and also take into account of sampled estimates of first-order and second-order information. The Monte Carlo tree in our approach avoids the usual fixed combinatorial patterns in growing the tree, and aggressively zooms into the promising regions, while still balancing exploration and exploitation. We evaluate the proposed algorithms on high-dimensional non-convex optimization benchmarks against competitive baselines and analyze the effects of the hyper parameters.

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Tang, Qiao, Hong Xie, Yunni Xia, Jia Lee, and Qingsheng Zhu. "Robust Contextual Bandits via Bootstrapping." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 12182–89. http://dx.doi.org/10.1609/aaai.v35i13.17446.

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Upper confidence bound (UCB) based contextual bandit algorithms require one to know the tail property of the reward distribution. Unfortunately, such tail property is usually unknown or difficult to specify in real-world applications. Using a tail property heavier than the ground truth leads to a slow learning speed of the contextual bandit algorithm, while using a lighter one may cause the algorithm to diverge. To address this fundamental problem, we develop an estimator (evaluated from historical rewards) for the contextual bandit UCB based on the multiplier bootstrapping technique. We first establish sufficient conditions under which our estimator converges asymptotically to the ground truth of contextual bandit UCB. We further derive a second order correction for our estimator so as to obtain its confidence level with a finite number of rounds. To demonstrate the versatility of the estimator, we apply it to design a BootLinUCB algorithm for the contextual bandit. We prove that the BootLinUCB has a sub-linear regret upper bound and also conduct extensive experiments to validate its superior performance.

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Wang, Zhi Ming. "Reliability Assessment Test Design for Numerical Control Machine Tools." Advanced Materials Research 542-543 (June 2012): 1218–21. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1218.

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According to the required interval estimation precision of mean time between failures (MTBF) and reliability for numerical control (NC) machine tools, where failure processes can be described by the power law process model, the minimal testing truncated time and sample size of the reliability assessment test are given using Fisher information matrix method. The results show that there are two main factors which affect the truncated time and sample size. They are the log ratio of upper bound to lower bound and confidence of interval estimation for reliability indices, respectively. Among them, the minimal sample size increases significantly as the improvement of required precision of interval estimation for reliability indices and confidence increases, but the minimal testing truncated time is less affected by these two factors.

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Herring, T. A., C. R. Gwinn, B. A. Buffett, and I. I. Shapiro. "Bound on the amplitude of the Earth's free core–nutation." Symposium - International Astronomical Union 128 (1988): 293–99. http://dx.doi.org/10.1017/s0074180900119643.

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We analyzed six years of very–long–baseline interferometry (VLBI) data and determined corrections to the coefficients of the seven terms with the largest amplitudes in the IAU 1980 nutation series. Our analysis yields results consistent with earlier analyses of smaller sets of VLBI data, within the uncertainties of the latter. Here, we restrict discussion to the freely excited core–nutation or “free core–nutation” (FCN). Our analysis yields an estimate of 0.33 ± 0.12 mas for an assumed constant amplitude of the FCN, which allows us to place an upper bound on it of 0.6 mas (99.5% confidence limit). We also studied possible temporal variations of the complex amplitude of the FCN by modeling it as a stochastic process with a white noise excitation. We detected no statistically significant variations of this amplitude for the six–year interval spanned by the VLBI data. However, in the neighborhood of one cycle per day, the power spectral density of the atmospheric surface loading is estimated from global weather data to be 0.24 (g cm−2)2 day, about five times larger than the largest such power spectral density that would be consistent with the upper bound on the amplitude of the FCN placed by the VLBI data. Thus, we conclude that this estimate is too high and that, if the FCN were excited by surface loads with frequencies near one cycle per day, then the power spectral density of these loads must be <0.06 (g cm−2)2 day (99.9% confidence limit).

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Revka, V. M., and L. I. Chyrko. "Comparison of Маster curve with normative method of estimating WWER-1000 reactor pressure vessel metal fracture toughness." Nuclear Physics and Atomic Energy 25, no. 1 (March 25, 2024): 58–65. http://dx.doi.org/10.15407/jnpae2024.01.058.

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The paper considers the generalized temperature dependence of the stress intensity factor KJC in the WWER-1000 reactor pressure vessel metal, which was derived based on the surveillance test data for fracture toughness. The consistency of the shape of the Master curve and 95 % upper and lower confidence bounds with the experimental dependence of crack resistance parameters on temperature was studied. From the point of view of the level of conservatism and acceptability, a comparison of the normative curves of fracture toughness according to NPRR (Nuclear Power Rules and Regulations) G-7-002-86 and SOU NNEGC (Standard of organization of Ukraine, National Nuclear Energy Generating Company) 177:2019 with a 5 % confidence bound of the Master curve was carried out. The possibility of using RT0 temperature as a temperature index instead of TK for the normative fracture toughness curve of SOU NNEGC 177:2019 was considered.

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Wu, Chenyue. "Comparative analysis of the KL-UCB and UCB algorithms: Delving into complexity and performance." Applied and Computational Engineering 53, no. 1 (March 28, 2024): 39–47. http://dx.doi.org/10.54254/2755-2721/53/20241221.

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This paper embarks on a meticulous comparative exploration of two venerable algorithms often invoked in multi-armed bandit problems: the Kullback-Leibler Upper Confidence Bound (KL-UCB) and the generic Upper Confidence Bound (UCB) algorithms. Initially, a comprehensive discourse is presented, elucidating the definition, evolution, and real-world applications of both algorithms. The crux of the study then shifts to a side-by-side comparison, weighing the regret performance and time complexities when applied to a quintessential movie rating dataset. In the trenches of practical implementations, addressing multi-armed bandit problems invariably demands extensive training. Consequently, even seemingly minor variations in algorithmic complexity can usher in pronounced differences in computational durations and resource utilization. This inherent intricacy prompts introspection: Is the potency of a given algorithm in addressing diverse practical quandaries commensurate with its inherent complexity. By juxtaposing the KL-UCB and UCB algorithms, this study not only highlights their relative merits and demerits but also furnishes insights that could serve as catalysts for further refinement and optimization. The overarching aim is to cultivate an informed perspective, guiding practitioners in choosing or fine-tuning algorithms tailored to specific applications without incurring undue computational overheads.

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Oh, Min-hwan, and Garud Iyengar. "Multinomial Logit Contextual Bandits: Provable Optimality and Practicality." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9205–13. http://dx.doi.org/10.1609/aaai.v35i10.17111.

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We consider a sequential assortment selection problem where the user choice is given by a multinomial logit (MNL) choice model whose parameters are unknown. In each period, the learning agent observes a d-dimensional contextual information about the user and the N available items, and offers an assortment of size K to the user, and observes the bandit feedback of the item chosen from the assortment. We propose upper confidence bound based algorithms for this MNL contextual bandit. The first algorithm is a simple and practical method that achieves an O(d√T) regret over T rounds. Next, we propose a second algorithm which achieves a O(√dT) regret. This matches the lower bound for the MNL bandit problem, up to logarithmic terms, and improves on the best-known result by a √d factor. To establish this sharper regret bound, we present a non-asymptotic confidence bound for the maximum likelihood estimator of the MNL model that may be of independent interest as its own theoretical contribution. We then revisit the simpler, significantly more practical, first algorithm and show that a simple variant of the algorithm achieves the optimal regret for a broad class of important applications.

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Cheung, Wang Chi, and David Simchi-Levi. "Sampling-Based Approximation Schemes for Capacitated Stochastic Inventory Control Models." Mathematics of Operations Research 44, no. 2 (May 2019): 668–92. http://dx.doi.org/10.1287/moor.2018.0940.

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We study the classical multiperiod capacitated stochastic inventory control problems in a data-driven setting. Instead of assuming full knowledge of the demand distributions, we assume that the demand distributions can only be accessed through drawing random samples. Such data-driven models are ubiquitous in practice, where the cumulative distribution functions of the underlying random demand are either unavailable or too complex to work with. We consider the sample average approximation (SAA) method for the problem and establish an upper bound on the number of samples needed for the SAA method to achieve a near-optimal expected cost, under any level of required accuracy and prespecified confidence probability. The sample bound is polynomial in the number of time periods as well as the confidence and accuracy parameters. Moreover, the bound is independent of the underlying demand distributions. However, the SAA requires solving the SAA problem, which is #P-hard. Thus, motivated by the SAA analysis, we propose a polynomial time approximation scheme that also uses polynomially many samples. Finally, we establish a lower bound on the number of samples required to solve this data-driven newsvendor problem to near-optimality.

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Jakob, Alexander M., Simon G. Robson, Vivien Schmitt, Vincent Mourik, Matthias Posselt, Daniel Spemann, Brett C. Johnson, et al. "Deterministic Shallow Dopant Implantation in Silicon with Detection Confidence Upper‐Bound to 99.85% by Ion–Solid Interactions." Advanced Materials 34, no. 3 (November 12, 2021): 2103235. http://dx.doi.org/10.1002/adma.202103235.

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Dimovski, Radomir, Robert Teitge, Nicholas Bolz, Patrick Schafer, Vamsy Bobba, and Rahul Vaidya. "Elimination of the Femoral Neck in Measuring Femoral Version Allows for Less Variance in Interobserver Reliability." Medicina 57, no. 12 (December 14, 2021): 1363. http://dx.doi.org/10.3390/medicina57121363.

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Background and Objectives: Producing consistent measures of femoral version amongst observers are necessary to allow for an assessment of version for possible corrective procedures. The purpose of this study was to compare two computed tomography (CT)-based techniques for the reliability of measuring femoral version amongst observers. Materials and Methods: Review was performed for 15 patients post-femoral nailing for comminuted (Winquist III and IV) femoral shaft fractures where CT scanograms were obtained. Two CT-based techniques were utilized to measure femoral version by five observers. Results: The mean femoral version, when utilizing a proximal line drawn down the center of the femoral head-neck through CT, was 9.50 ± 4.82°, while the method utilizing the head and shaft at lesser trochanter centers produced a mean version of 18.73 ± 2.69°. A significant difference was noted between these two (p ≤ 0.001). The method of measuring in the center of the femoral head and neck produced an intraclass correlation coefficient (ICC) of 0.960 with a 95% confidence interval lower bound of 0.909 and upper bound of 0.982. For the method assessing version via the center of the head and shaft at the lesser trochanter region, the ICC was 0.993 with a 95% confidence interval lower bound of 0.987 and an upper bound of 0.996. Conclusions: The method of measuring version proximally through a CT image of the femoral head–neck versus overlaying the femoral head with the femoral shaft at the most prominent aspect of the lesser trochanter produces differing version measurements by roughly 10° while yielding an almost perfect interobserver reliability in the new technique. Both techniques result in significantly high interobserver reliability.

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Wang, Kai, Lily Xu, Aparna Taneja, and Milind Tambe. "Optimistic Whittle Index Policy: Online Learning for Restless Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 10131–39. http://dx.doi.org/10.1609/aaai.v37i8.26207.

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Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on whether that arm is pulled. Solving RMABs requires information on transition dynamics, which are often unknown upfront. To plan in RMAB settings with unknown transitions, we propose the first online learning algorithm based on the Whittle index policy, using an upper confidence bound (UCB) approach to learn transition dynamics. Specifically, we estimate confidence bounds of the transition probabilities and formulate a bilinear program to compute optimistic Whittle indices using these estimates. Our algorithm, UCWhittle, achieves sublinear O(H \sqrt{T log T}) frequentist regret to solve RMABs with unknown transitions in T episodes with a constant horizon H. Empirically, we demonstrate that UCWhittle leverages the structure of RMABs and the Whittle index policy solution to achieve better performance than existing online learning baselines across three domains, including one constructed from a real-world maternal and childcare dataset.

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Li, Frederick P., Wick R. Williams, Kathreen Gimbrere, Francoise Flamant, Daniel M. Green, and Anna T. Meadows. "Heritable Fraction of Unilateral Wilms Tumor." Pediatrics 81, no. 1 (January 1, 1988): 147–49. http://dx.doi.org/10.1542/peds.81.1.147.

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Heritability of the unilateral-sporadic (non-familial) form of Wilms tumor was examined in the offspring of 96 long-term survivors of the neoplasm. No Wilms tumor has developed in any of the 179 offspring of these patients. The maximum likelihood estimate of a hereditary Wilms tumor in our patients is zero and the corresponding 95% upper confidence limit ranges between 0.06 and 0.11, depending on penetrance. Among their offspring, the 95% upper bound of the risk of Wilms tumor is 0.02. These figures can be applied in genetic counseling of other survivors of unilateral-sporadic Wilms tumor.

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Mao, Yifei. "The investigation related to the influence of dimension manipulation on regret performance based on the upper confidence bound algorithm." Applied and Computational Engineering 34, no. 1 (January 22, 2024): 245–51. http://dx.doi.org/10.54254/2755-2721/34/20230337.

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This paper aims to investigate the effects of dimension manipulation on the performance of a recommendation algorithm applied to a dataset of restaurant reviews. In this paper, the Zomato dataset which contains restaurant reviews and other relevant information in Bangalore City was used. This paper extracted ratings that each user gave for each restaurant from the list of user feedback for each restaurant. These different ratings were stored as a core feature that was used for the restaurant recommendation algorithm to estimate the true mean rating of each restaurant. Upper Confidence Bound bandit algorithm was used as the restaurant recommendation algorithm to find the restaurant with the highest average rating in the dataset. Dimension raising and dimension reduction were used as ways to manipulate dimension in this paper. Principal Component Analysis was used as the technique to reduce feature dimension in the dataset. It reduced features into two principal components and the first principal component was used in place of the original core feature. Dimension raising was implemented based on the original core feature and another feature that correlated with it the most. The product of these two features was used in place of the original core feature. The experimental results suggest that dimension manipulation leads to decreased regret performance when employing the Upper Confidence Bound algorithm for recommendation. Intriguingly, within the realm of dimension manipulation, dimension reduction exhibited a more adverse impact on regret performance compared to dimension raising.

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Schwartz, Marc D., Heiddis B. Valdimarsdottir, Beth N. Peshkin, Jeanne Mandelblatt, Rachel Nusbaum, An-Tsun Huang, Yaojen Chang, et al. "Randomized Noninferiority Trial of Telephone Versus In-Person Genetic Counseling for Hereditary Breast and Ovarian Cancer." Journal of Clinical Oncology 32, no. 7 (March 1, 2014): 618–26. http://dx.doi.org/10.1200/jco.2013.51.3226.

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Purpose Although guidelines recommend in-person counseling before BRCA1/BRCA2 gene testing, genetic counseling is increasingly offered by telephone. As genomic testing becomes more common, evaluating alternative delivery approaches becomes increasingly salient. We tested whether telephone delivery of BRCA1/2 genetic counseling was noninferior to in-person delivery. Patients and Methods Participants (women age 21 to 85 years who did not have newly diagnosed or metastatic cancer and lived within a study site catchment area) were randomly assigned to usual care (UC; n = 334) or telephone counseling (TC; n = 335). UC participants received in-person pre- and post-test counseling; TC participants completed all counseling by telephone. Primary outcomes were knowledge, satisfaction, decision conflict, distress, and quality of life; secondary outcomes were equivalence of BRCA1/2 test uptake and costs of delivering TC versus UC. Results TC was noninferior to UC on all primary outcomes. At 2 weeks after pretest counseling, knowledge (d = 0.03; lower bound of 97.5% CI, −0.61), perceived stress (d = −0.12; upper bound of 97.5% CI, 0.21), and satisfaction (d = −0.16; lower bound of 97.5% CI, −0.70) had group differences and confidence intervals that did not cross their 1-point noninferiority limits. Decision conflict (d = 1.1; upper bound of 97.5% CI, 3.3) and cancer distress (d = −1.6; upper bound of 97.5% CI, 0.27) did not cross their 4-point noninferiority limit. Results were comparable at 3 months. TC was not equivalent to UC on BRCA1/2 test uptake (UC, 90.1%; TC, 84.2%). TC yielded cost savings of $114 per patient. Conclusion Genetic counseling can be effectively and efficiently delivered via telephone to increase access and decrease costs.

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Chen, Han, Rui Chen, Shaniel Bernard, and Imran Rahman. "US hotel industry revenue: an ARDL bounds testing approach." International Journal of Contemporary Hospitality Management 31, no. 4 (April 8, 2019): 1720–43. http://dx.doi.org/10.1108/ijchm-01-2018-0031.

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Purpose This study aims to develop a parsimonious model to estimate US aggregate hotel industry revenue using domestic trips, consumer confidence index, international inbound trips, personal consumption expenditure and number of hotel rooms as predictor variables. Additionally, the study applied the model in six sub-segments of the hotel industry – luxury, upper upscale, upscale, upper midscale, midscale and economy. Design/methodology/approach Using monthly aggregate data from the past 22 years, the study adopted the auto-regressive distribute lags (ARDL) approach in developing the estimation model. Unit root analysis and cointegration test were further utilized. The model showed significant utility in accurately estimating aggregate hotel industry and sub-segment revenue. Findings All predictor variables except number of rooms showed significant positive influences on aggregate hotel industry revenue. Substantial variations were noted regarding estimating sub-segment revenue. Consumer confidence index positively affected all sub-segment revenues, except for upper upscale hotels. Inbound trips by international tourists and personal consumption expenditure positively influenced revenue for all sub-segments but economy hotels. Domestic trips by US residents added significant explanatory power to only upper upscale, upscale and economy hotel revenue. Number of hotel rooms only had significant negative effect on luxury and upper upscale hotel sub-segment revenues. Practical implications Hotel operators can make marketing and operating decisions regarding pricing, inventory allocation and strategic management based on the revenue estimation models specific to their segments. Originality/value It is the first study that adopted the ARDL bound approach and analyzed the predictive capacity of macroeconomic variables on aggregate hotel industry and sub-segment revenue.

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Journal articles on the topic 'Upper Confidence Bound'

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