scholarly journals Robust Non-Parametric Mortality and Fertility Modelling and Forecasting: Gaussian Process Regression Approaches

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 207-227
Author(s):  
Ka Kin Lam ◽  
Bo Wang
Keyword(s):  
Gaussian Process ◽  
Forecast Accuracy ◽  
Gaussian Process Regression ◽  
Developed Countries ◽  
Rapid Decline ◽  
Demographic Modelling ◽  
The Government ◽  
Over Time ◽  
Modelling And Forecasting ◽  
Non Parametric

A rapid decline in mortality and fertility has become major issues in many developed countries over the past few decades. An accurate model for forecasting demographic movements is important for decision making in social welfare policies and resource budgeting among the government and many industry sectors. This article introduces a novel non-parametric approach using Gaussian process regression with a natural cubic spline mean function and a spectral mixture covariance function for mortality and fertility modelling and forecasting. Unlike most of the existing approaches in demographic modelling literature, which rely on time parameters to determine the movements of the whole mortality or fertility curve shifting from one year to another over time, we consider the mortality and fertility curves from their components of all age-specific mortality and fertility rates and assume each of them following a Gaussian process over time to fit the whole curves in a discrete but intensive style. The proposed Gaussian process regression approach shows significant improvements in terms of forecast accuracy and robustness compared to other mainstream demographic modelling approaches in the short-, mid- and long-term forecasting using the mortality and fertility data of several developed countries in the numerical examples.

scholarly journals An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Lu Cheng ◽  
Siddharth Ramchandran ◽  
Tommi Vatanen ◽  
Niina Lietzén ◽  
Riitta Lahesmaa ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Regression Model ◽  
Gaussian Process ◽  
Parametric Analysis ◽  
Gaussian Process Regression ◽  
Non Parametric

scholarly journals Development of a non-parametric Gaussian process model in the three-dimensional equilibrium reconstruction code V3FIT

2020 ◽  
Vol 86 (1) ◽  
Author(s):  
Eric C. Howell ◽  
J. D. Hanson
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Gaussian Process Regression ◽  
Equilibrium Reconstruction ◽  
Equilibrium Profiles ◽  
Mean Function ◽  
Best Fit ◽  
Non Parametric

A non-parametric Gaussian process regression model is developed in the three-dimensional equilibrium reconstruction code V3FIT. A Gaussian process is a normal distribution of functions that is uniquely defined by specifying a mean function and covariance kernel function. Gaussian process regression assumes that an unknown profile belongs to a particular Gaussian process and uses Bayesian analysis to select the function the give the best fit to measured data. The implementation in V3FIT uses a hybrid representation where Gaussian processes are used to infer some of the equilibrium profiles and standard parametric techniques are used to infer the remaining profiles. The implementation of the Gaussian process is tested using both synthetic data and experimental data from multiple machines.

scholarly journals A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions

2016 ◽  
Author(s):  
Eric Schulz ◽  
Maarten Speekenbrink ◽  
Andreas Krause
Keyword(s):  
Gaussian Process ◽  
Gaussian Processes ◽  
Gaussian Process Regression ◽  
Risk Averse ◽  
Exploration And Exploitation ◽  
Regression Modelling ◽  
Regression Problems ◽  
Exploration Exploitation ◽  
Psychological Experiments ◽  
Non Parametric

AbstractThis tutorial introduces the reader to Gaussian process regression as an expressive tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial aims to provide an accessible introduction to these techniques. We will introduce Gaussian processes which generate distributions over functions used for Bayesian non-parametric regression, and demonstrate their use in applications and didactic examples including simple regression problems, a demonstration of kernel-encoded prior assumptions and compositions, a pure exploration scenario within an optimal design framework, and a bandit-like exploration-exploitation scenario where the goal is to recommend movies. Beyond that, we describe a situation modelling risk-averse exploration in which an additional constraint (not to sample below a certain threshold) needs to be accounted for. Lastly, we summarize recent psychological experiments utilizing Gaussian processes. Software and literature pointers are also provided.

Housing Inflation: Policy Guidelines from Shariah Perspective

2019 ◽  
Vol 16 (1) ◽  
pp. 70-81
Author(s):  
Azrul Azlan Iskandar Mirza ◽  
Asmaddy Haris ◽  
Ainulashikin Marzuki ◽  
Ummi Salwa Ahmad Bustamam ◽  
Hamdi Hakiem Mudasir ◽  
...  
Keyword(s):  
Content Analysis ◽  
Housing Prices ◽  
Construction Materials ◽  
Housing Price ◽  
Developed Countries ◽  
Control Element ◽  
Recent Issue ◽  
Analysis Methodology ◽  
The Government ◽  
Developing And Developed Countries

The soaring housing prices in Malaysia is not a recent issue. It is a global phenomenon especially in developing and developed countries, driven by factors including land price, location, construction materials cost, demand, and speculation. This issue demands immediate attention as it affects the younger generation, most of whom could not afford to buy their own house. The government has taken many initiatives and introduced regulations to ensure that housing prices are within the affordable range. This article aims to introduce a housing price control element from the Shariah perspective, as an alternative solution for all parties involved in this issue. It adopts content analysis methodology on policy from Shariah approved sources.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

SAMPL6 Challenge Results from pKa Predictions Based on a General Gaussian Process Model

2018 ◽  
Author(s):  
Caitlin C. Bannan ◽  
David Mobley ◽  
A. Geoff Skillman
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Molecular Graph ◽  
Gaussian Process Regression ◽  
Ionization State ◽  
Training Set ◽  
Physiochemical Properties ◽  
Quantile Plots ◽  
Physical And Chemical ◽  
Good Agreement

<div>A variety of fields would benefit from accurate pK<sub>a</sub> predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties.</div><div>Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pK<sub>a</sub>s of 24 drug like small molecules.</div><div>We recently built a general model for predicting pK<sub>a</sub>s using a Gaussian process regression trained using physical and chemical features of each ionizable group.</div><div>Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton.</div><div>These features are fed into a Scikit-learn Gaussian process to predict microscopic pK<sub>a</sub>s which are then used to analytically determine macroscopic pK<sub>a</sub>s.</div><div>Our Gaussian process is trained on a set of 2,700 macroscopic pK<sub>a</sub>s from monoprotic and select diprotic molecules.</div><div>Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge.</div><div>Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic.</div><div>Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. </div><div>Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy.</div><div>The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable. </div>

PUBLIC DIPLOMACY RELATIONS OF UZBEKISTAN WITH MALAYSIA DURING THE YEARS OF INDEPENDENCE

2020 ◽  
Vol 11 (3) ◽  
pp. 17-25
Author(s):  
Otabek Alimardonov ◽  
Keyword(s):  
Southeast Asia ◽  
Public Diplomacy ◽  
Developed Countries ◽  
The Government

Today, Malaysia is one of the most developed countries in Southeast Asia and a close partner of Uzbekistan in the region. Taking into account the peculiarities of the development and achievements of the countries of Southeast Asia, the Government of Uzbekistan from the first years of independence has paid special attention to the establishment and development of cooperation with Malaysia

Foreign Trade Regimes and Economic Development: India. By Jagdish N. Bhagwati and T.N. Sirinivasan. New York Columbia University Press (Published by the National Bureau of Economic Research 1975) 261 pages.

1977 ◽  
Vol 16 (1) ◽  
pp. 112-114
Author(s):  
Abdur Razzaq Shahid
Keyword(s):  
New York ◽  
Economic Development ◽  
Foreign Trade ◽  
Developed Countries ◽  
Exchange Rate Policy ◽  
Economic Research ◽  
National Bureau ◽  
Columbia University ◽  
The Government ◽  
Exchange Control

This volume on India is one of a series of research projects on exchange control, liberalization, and economic development, undertaken for many less developed countries. The study deals with three major topics: exchange control, liberalization, and growth. First, under 'The Anatomy of Exchange Control', the methods of allocation and intervention in the foreign trade and payments practised by the government during the restrictive period 1956-66 and their economic impact are discussed. Then, a detailed analysis of the 'Liberalization Episode' which covers the policies in the period 1966-68, including the June 1966 devaluation, and the episode's effect on price level, economic activity, and exports is given. Finally, the overall growth effects of the foreign trade regime (broadly defined as exchange rate policy plus the frame-work of relevant domestic policies such as industrial licensing), and their possible contribution to India's rather unsatisfactory economic performance are examined.

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