A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models

This paper provides a step-by-step guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed by Imai Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm, which solves the DDP model and estimates its structural parameters simultaneously. The main computational advantage of this estimation algorithm is the efficient use of information obtained from the past iterations. In the conventional Nested Fixed Point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the Bayesian Dynamic Programming algorithm extensively uses the computational results obtained from the past iterations to help solving the DDP model at the current iterated parameter values. Consequently, it significantly alleviates the computational burden of estimating a DDP model. We carefully discuss how to implement the algorithm in practice, and use a simple dynamic store choice model to illustrate how to apply this algorithm to obtain parameter estimates.Bayesian Dynamic Programming, Discrete Choice Dynamic Programming, Markov Chain Monte Carlo

Cyclic Strength of Undisturbed Mine Tailings

In order to update existing regulations for the seismic design of tailings retention dikes, extensive investigations were undertaken for 15 existing tailings dams throughout Japan. Undisturbed samples procured from the tailings disposal ponds were tested ln the laboratory to determine the cyclic strength of the in-situ tailings deposits. The results of cyclic triaxial tests on these materials are summarized by means of empirical formulae which are recommended for incorporation in the new seismic design code for the tailings dams

A practitioner's guide to Bayesian estimation of discrete choice dynamic programming models

This paper provides a step-by-step guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed in Imai, Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm, which solves the DDP model and estimates its structural parameters simultaneously. The main computational advantage of this estimation algorithm is the efficient use of information obtained from the past iterations. In the conventional Nested Fixed Point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the Bayesian Dynamic Programming algorithm extensively uses the computational results obtained from the past iterations to help solving the DDP model at the current iterated parameter values. Consequently, it significantly alleviates the computational burden of estimating a DDP model. We carefully discuss how to implement the algorithm in practice, and use a simple dynamic store choice model to illustrate how to apply this algorithm to obtain parameter estimates

Design of Energy-Efficient Artificial Noise for Physical Layer Security in Visible Light Communications

This paper studies the design of energy-efficient artificial noise (AN) schemes in the context of physical layer security in visible light communications (VLC). Two different transmission schemes termed $\textit{selective AN-aided single-input single-output (SISO)}$ and $\textit{AN-aided multiple-input single-output (MISO)}$ are examined and compared in terms of secrecy energy efficiency (SEE). In the former, the closest LED luminaire to the legitimate user (Bob) is the information-bearing signal's transmitter. At the same time, the rest of the luminaries act as jammers transmitting AN to degrade the channels of eavesdroppers (Eves). In the latter, the information-bearing signal and AN are combined and transmitted by all luminaries. When Eves' CSI is unknown, an indirect design to improve the SEE is formulated by maximizing Bob's channel's energy efficiency. A low-complexity design based on the zero-forcing criterion is also proposed. In the case of known Eves' CSI, we study the design that maximizes the minimum SEE among those corresponding to all eavesdroppers. At their respective optimal SEEs, simulation results reveal that when Eves' CSI is unknown, the selective AN-aided SISO transmission can archive twice better SEE as the AN-aided MISO does. In contrast, when Eves' CSI is known, the AN-aided MISO outperforms by 30%

Breath Hydrogen Gas Concentration Linked to Intestinal Gas Distribution and Malabsorption in Patients with Small-bowel Pseudo-obstruction

Background: The patient with colonic obstruction may frequently have bacterial overgrowth and increased breath hydrogen (H2) levels because the bacterium can contact with food residues for longer time. We experienced two cases with intestinal obstruction whose breath H2 concentrations were measured continuously.Case 1: A 70-year-old woman with small bowel obstruction was treated with a gastric tube. When small bowel gas decreased and colonic gas was demonstrated on the plain abdominal radiograph, the breath H2 concentration increased to 6 ppm and reduced again shortly.Case 2: A 41-year-old man with functional small bowel obstruction after surgical treatment was treated with intravenous administration of erythromycin. Although the plain abdominal radiograph demonstrated a decrease of small-bowel gas, the breath H2 gas kept the low level. After a clear-liquid meal was supplied, fasting breath H2 concentration increased rapidly to 22 ppm and gradually decreased to 9 ppm despite the fact that the intestinal gas was unchanged on X-ray. A rapid increase of breath H2 concentration may reflect the movement of small bowel contents to the colon in patients with small-bowel pseudo-obstruction or malabsorption following diet progression.Conclusions: Change in breath H2 concentration had a close association with distribution and movement of intestinal gas

Proteome analysis of human metaphase chromosomes

Susumu Uchiyama, Shouhei Kobayashi, Hideaki Takata, Takeshi Ishihara, Naoto Hori, Tsunehito Higashi, Kayoko Hayashihara, Takefumi Sone, Daisuke Higo, Takashi Nirasawa, Toshifumi Takao, Sachihiro Matsunaga, Kiichi Fukui. Proteome Analysis of Human Metaphase Chromosomes. Journal of Biological Chemistry, Volume 280, Issue 17, 2005, Pages 16994-17004. https://doi.org/10.1074/jbc.M412774200