Author: Sriraman Venkataraman
Publisher:
ISBN:
Category :
Languages : en
Pages : 316

Get Book Here

Book Description

Author: Söhnke M. Bartram
Publisher: CFA Institute Research Foundation
ISBN: 195292703X
Category : Business & Economics
Languages : en
Pages : 95

Get Book Here

Book Description
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Author: Michael P. Clements
Publisher: OUP USA
ISBN: 0195398645
Category : Business & Economics
Languages : en
Pages : 732

Get Book Here

Book Description
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.

Author: Rina Dechter
Publisher:
ISBN: 9781904987659
Category : Computers
Languages : en
Pages : 565

Get Book Here

Book Description
The field of Artificial Intelligence has changed a great deal since the 80s, and arguably no one has played a larger role in that change than Judea Pearl. Judea Pearl's work made probability the prevailing language of modern AI and, perhaps more significantly, it placed the elaboration of crisp and meaningful models, and of effective computational mechanisms, at the center of AI research. This book is a collection of articles in honor of Judea Pearl, written by close colleagues and former students. Its three main parts, heuristics, probabilistic reasoning, and causality, correspond to the titles of the three ground-breaking books authored by Judea, and are followed by a section of short reminiscences. In this volume, leading authors look at the state of the art in the fields of heuristic, probabilistic, and causal reasoning, in light of Judea's seminal contributors. The authors list include Blai Bonet, Eric Hansen, Robert Holte, Jonathan Schaeffer, Ariel Felner, Richard Korf, Austin Parker, Dana Nau, V. S. Subrahmanian, Hector Geffner, Ira Pohl, Adnan Darwiche, Thomas Dean, Rina Dechter, Bozhena Bidyuk, Robert Matescu, Emma Rollon, Michael I. Jordan, Michael Kearns, Daphne Koller, Brian Milch, Stuart Russell, Azaria Paz, David Poole, Ingrid Zukerman, Carlos Brito, Philip Dawid, Felix Elwert, Christopher Winship, Michael Gelfond, Nelson Rushton, Moises Goldszmidt, Sander Greenland, Joseph Y. Halpern, Christopher Hitchcock, David Heckerman, Ross Shachter, Vladimir Lifschitz, Thomas Richardson, James Robins, Yoav Shoham, Peter Spirtes, Clark Glymour, Richard Scheines, Robert Tillman, Wolfgang Spohn, Jian Tian, Ilya Shpitser, Nils Nilsson, Edward T. Purcell, and David Spiegelhalter.

Author: Debasis Kundu
Publisher: Academic Press
ISBN: 0081012403
Category : Mathematics
Languages : en
Pages : 188

Get Book Here

Book Description
Analysis of Step-Stress Models: Existing Results and Some Recent Developments describes, in detail, the step-stress models and related topics that have received significant attention in the last few years. Although two books, Bagdonavicius and Nikulin (2001) and Nelson (1990), on general accelerated life testing models are available, no specific book is available on step-stress models. Due to the importance of this particular topic, Balakrishnan (2009) provided an excellent review for exponential step-stress models. The scope of this book is much more, providing the inferential issues for different probability models, both from the frequentist and Bayesian points-of-view. Explains the different distributions of the Cumulative Exposure Mode Covers many different models used for step-stress analysis Discusses Step-stress life testing under the competing or complementary risk model

Author: Patricia Freitag Ericsson
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 284

Get Book Here

Book Description
The current trend toward machine-scoring of student work, Ericsson and Haswell argue, has created an emerging issue with implications for higher education across the disciplines, but with particular importance for those in English departments and in administration. The academic community has been silent on the issue—some would say excluded from it—while the commercial entities who develop essay-scoring software have been very active. Machine Scoring of Student Essays is the first volume to seriously consider the educational mechanisms and consequences of this trend, and it offers important discussions from some of the leading scholars in writing assessment. Reading and evaluating student writing is a time-consuming process, yet it is a vital part of both student placement and coursework at post-secondary institutions. In recent years, commercial computer-evaluation programs have been developed to score student essays in both of these contexts. Two-year colleges have been especially drawn to these programs, but four-year institutions are moving to them as well, because of the cost-savings they promise. Unfortunately, to a large extent, the programs have been written, and institutions are installing them, without attention to their instructional validity or adequacy. Since the education software companies are moving so rapidly into what they perceive as a promising new market, a wider discussion of machine-scoring is vital if scholars hope to influence development and/or implementation of the programs being created. What is needed, then, is a critical resource to help teachers and administrators evaluate programs they might be considering, and to more fully envision the instructional consequences of adopting them. And this is the resource that Ericsson and Haswell are providing here.

Author: Gilles Dufrénot
Publisher: Springer Nature
ISBN: 3030542521
Category : Business & Economics
Languages : en
Pages : 387

Get Book Here

Book Description
The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models. The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.

Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262331713
Category : Computers
Languages : en
Pages : 350

Get Book Here

Book Description
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Author: Peter D. Hoff
Publisher: Springer Science & Business Media
ISBN: 0387924078
Category : Mathematics
Languages : en
Pages : 270

Get Book Here

Book Description
A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

Author: Daniel Lederman
Publisher: World Bank Publications
ISBN: 0821384910
Category : Business & Economics
Languages : en
Pages : 153

Get Book Here

Book Description
Does what economies export matter for development? If so, can industrial policies improve on the export basket generated by the market? This book approaches these questions from a variety of conceptual and policy viewpoints. Reviewing the theoretical arguments in favor of industrial policies, the authors first ask whether existing indicators allow policy makers to identify growth-promoting sectors with confidence. To this end, they assess, and ultimately cast doubt upon, the reliability of many popular indicators advocated by proponents of industrial policy. Second, and central to their critique, the authors document extraordinary differences in the performance of countries exporting seemingly identical products, be they natural resources or 'high-tech' goods. Further, they argue that globalization has so fragmented the production process that even talking about exported goods as opposed to tasks may be misleading. Reviewing evidence from history and from around the world, the authors conclude that policy makers should focus less on what is produced, and more on how it is produced. They analyze alternative approaches to picking winners but conclude by favoring 'horizontal-ish' policies--for instance, those that build human capital or foment innovation in existing and future products—that only incidentally favor some sectors over others.