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Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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1

Walsh, John Leclerc. Extraction of single channel current from correlated noise via a hidden Markov model. [s.l: s.n.], 1992.

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2

Penny, D. Modeling the covarion model of molecular evolution by hidden Markov chains. Palmerston North, N.Z: Massey University College of Sciences, 1998.

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3

Jacobs, Patricia A. Estimation of the probability of a long time to the first entrance to a state in a semi-Markov model. Monterey, Calif: Naval Postgraduate School, 1987.

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4

Nguyen, Van O. Analysis of the U.S. Marine Corps' steady state Markov model for forecasting annual first-term enlisted classification requirements. Monterey, Calif: Naval Postgraduate School, 1997.

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5

Howes, Andrew, Xiuli Chen, Aditya Acharya, and Richard L. Lewis. Interaction as an Emergent Property of a Partially Observable Markov Decision Process. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0011.

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In this chapter we explore the potential advantages of modeling the interaction between a human and a computer as a consequence of a Partially Observable Markov Decision Process (POMDP) that models human cognition. POMDPs can be used to model human perceptual mechanisms, such as human vision, as partial (uncertain) observers of a hidden state are possible. In general, POMDPs permit a rigorous definition of interaction as the outcome of a reward maximizing stochastic sequential decision processes. They have been shown to explain interaction between a human and an environment in a range of scenarios, including visual search, interactive search and sense-making. The chapter uses these scenarios to illustrate the explanatory power of POMDPs in HCI. It also shows that POMDPs embrace the embodied, ecological and adaptive nature of human interaction.

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6

Sun, Shuying. Haplotype inference using a hidden Markov model with efficient Markov chain sampling. 2007, 2007.

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7

Miao, Qiang. Application of wavelets and hidden Markov model in condition-based maintenance. 2005.

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8

A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. Providence, USA: Brown University, 2019.

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9

Analysis of the U.S. Marine Corps' Steady State Markov Model for Forecasting Annual First-Term Enlisted Classification Requirements. Storming Media, 1997.

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10

Blee, Lisa, and Jean M. O'Brien. Monumental Mobility. University of North Carolina Press, 2019. http://dx.doi.org/10.5149/northcarolina/9781469648408.001.0001.

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Installed at Plymouth, Massachusetts, in 1921 to commemorate the tercentenary of the landing of the Pilgrims, Cyrus Dallin's statue Massasoit was intended to memorialize the Pokanoket Massasoit (leader) as a welcoming diplomat and participant in the mythical first Thanksgiving. But after the statue's unveiling, Massasoit began to move and proliferate in ways one would not expect of generally stationary monuments tethered to place. The plaster model was donated to the artist's home state of Utah and prominently displayed in the state capitol; half a century later, it was caught up in a surprising case of fraud in the fine arts market. Versions of the statue now stand on Brigham Young University's campus; at an urban intersection in Kansas City, Missouri; and in countless homes around the world in the form of souvenir statuettes. The surprising story of this monumental statue reveals much about the process of creating, commodifying, and reinforcing the historical memory of Indigenous people. Dallin's statue, set alongside the historical memory of the actual Massasoit and his mythic collaboration with the Pilgrims, shows otherwise hidden dimensions of American memorial culture: an elasticity of historical imagination, a tight-knit relationship between consumption and commemoration, and the twin impulses to sanitize and grapple with the meaning of settler-colonialism.

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11

Stricker, Frank. American Unemployment. University of Illinois Press, 2020. http://dx.doi.org/10.5622/illinois/9780252043154.001.0001.

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This book shows that full employment has been rare in the United States in the last 150 years; excessive unemployment has been the norm. Against prominent economists who argue that unemployment is voluntary choice, it shows by analysis and many stories that being unemployed is painful and not something people choose lightly. It argues that hidden unemployment and a continuing labor surplus help explain why average real wages in 2019 are not much above their level of the early 1970s. The book locates consequential ideas about unemployment on a continuum between two opposing views. The free-market view holds that except for external shocks or government mistakes, significant unemployment is rare. People can always find jobs. But the historical record tells another story. For example, with mostly laissez-faire conditions, there were six major depressions from 1873 through 1933.The opposing view is that the business system naturally generates excessive unemployment, and at times depressions with catastrophic levels of joblessness. The book shows how the second model fits past and present facts. It also argues that the official unemployment rate, whose creation in the 1940s was an advance for economic policy, underestimates real unemployment and lessens the impetus for job-creation programs. And that’s a problem. Because many employers are happy with a labor surplus, and because tax cuts for the rich do not create many good jobs, this book argues that only direct job creation by the federal government—financed partly by taxes on the rich—will bring high-wage full employment.

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12

Back, Kerry E. Learning. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0023.

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Continuous‐time filtering is explained, including the Kalman filter and filtering for a Markov chain with hidden states. Filtering theory is applied to analyze portfolio choice and equilibrium asset prices. When the expected return of an asset is unknown and is estimated from past returns, the myopic demand is a momentum strategy. When investors learn expected consumption growth from realized consumption growth, equilibrium prices are more sensitive to consumption shocks and the equity premium is higher. When the consumption growth rate follows a Markov chain with hidden states, return volatility tends to be higher when investors are less certain about which state the economy is in.

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13

Voutilainen, Atro. Part-of-Speech Tagging. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0011.

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This article outlines the recently used methods for designing part-of-speech taggers; computer programs for assigning contextually appropriate grammatical descriptors to words in texts. It begins with the description of general architecture and task setting. It gives an overview of the history of tagging and describes the central approaches to tagging. These approaches are: taggers based on handwritten local rules, taggers based on n-grams automatically derived from text corpora, taggers based on hidden Markov models, taggers using automatically generated symbolic language models derived using methods from machine tagging, taggers based on handwritten global rules, and hybrid taggers, which combine the advantages of handwritten and automatically generated taggers. This article focuses on handwritten tagging rules. Well-tagged training corpora are a valuable resource for testing and improving language model. The text corpus reminds the grammarian about any oversight while designing a rule.

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14

Samuelsson, Christer. Statistical Methods. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0019.

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Statistical methods now belong to mainstream natural language processing. They have been successfully applied to virtually all tasks within language processing and neighbouring fields, including part-of-speech tagging, syntactic parsing, semantic interpretation, lexical acquisition, machine translation, information retrieval, and information extraction and language learning. This article reviews mathematical statistics and applies it to language modelling problems, leading up to the hidden Markov model and maximum entropy model. The real strength of maximum-entropy modelling lies in combining evidence from several rules, each one of which alone might not be conclusive, but which taken together dramatically affect the probability. Maximum-entropy modelling allows combining heterogeneous information sources to produce a uniform probabilistic model where each piece of information is formulated as a feature. The key ideas of mathematical statistics are simple and intuitive, but tend to be buried in a sea of mathematical technicalities. Finally, the article provides mathematical detail related to the topic of discussion.

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15

Basu, Sanjay. Modeling Large-Scale Epidemics. Edited by Sanjay Basu. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.003.0009.

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Previous chapters ignored a critical aspect of modeling some major diseases: the infectious nature of many diseases. For infectious diseases, the risk of getting the disease is related to how many people are infectious at a given time: the more infectious people in the area, the higher the risk of infection among susceptible people. In a typical Markov model, we can’t account for this basic feature of infectious diseases because the risk of moving from one state (healthy) to another state (diseased) is assumed to be constant. In this chapter, the author introduces a simulation modeling framework that has been used for decades to simulate infectious disease epidemics.

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16

Back, Kerry E. Continuous-Time Markets. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0013.

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A continuous‐time model of a securities market is introduced. The intertemporal budget constraint is defined. SDF processes and prices of risks are defined and characterized. Many properties of SDF process are analogous to those in a single‐period model, including the relation to the risk‐free rate, orthogonal projections, the Hansen‐Jagannathan bound, and factor pricing. To value future cash flows using an SDF process, we need to assume a local martingale is a martingale. Sufficient conditions including Novikov’s condition are discussed. Use of the martingale representation theorem in a complete market to derive a portfolio that replicates a payoff is explained. A Markovian model is introduced, in which the investment opportunity set depends on state variables that form a Markov process.

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17

Laver, Michael, and Ernest Sergenti. Systematically Interrogating Agent-Based Models. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0004.

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This chapter develops the methods for designing, executing, and analyzing large suites of computer simulations that generate stable and replicable results. It starts with a discussion of the different methods of experimental design, such as grid sweeping and Monte Carlo parameterization. Next, it demonstrates how to calculate mean estimates of output variables of interest. It does so by first discussing stochastic processes, Markov Chain representations, and model burn-in. It focuses on three stochastic process representations: nonergodic deterministic processes that converge on a single state; nondeterministic stochastic processes for which a time average provides a representative estimate of the output variables; and nondeterministic stochastic processes for which a time average does not provide a representative estimate of the output variables. The estimation strategy employed depends on which stochastic process the simulation follows. Lastly, the chapter presents a set of diagnostic checks used to establish an appropriate sample size for the estimation of the means.

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18

Gershman, Samuel J. Reinforcement Learning and Causal Models. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.20.

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This chapter reviews the diverse roles that causal knowledge plays in reinforcement learning. The first half of the chapter contrasts a “model-free” system that learns to repeat actions that lead to reward with a “model-based” system that learns a probabilistic causal model of the environment, which it then uses to plan action sequences. Evidence suggests that these two systems coexist in the brain, both competing and cooperating with each other. The interplay of two systems allows the brain to negotiate a balance between cognitively cheap but inaccurate model-free algorithms and accurate but expensive model-based algorithms. The second half of the chapter reviews research on hidden state inference in reinforcement learning. The problem of inferring hidden states can be construed in terms of inferring the latent causes that give rise to sensory data and rewards. Because hidden state inference affects both model-based and model-free reinforcement learning, causal knowledge impinges upon both systems.

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19

Hans, Steiner, Daniels Whitney, Kelly Michael, and Stadler Christina. Taxonomy, Classification, and Diagnosis of Disruptive Behavior Disorders. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190265458.003.0002.

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This chapter traces the development of diagnoses attempting to capture antisocial and aggressive behavior. The chapter provides a careful discussion of the advantages of the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases systems and their diagnostic grouping. Tracing the processes by which these diagnoses were created, the hidden and obvious problems in the current taxonomy are laid bare. The model of developmental psychopathology, of which disruptive behavior disorders arguably have been called a model disorder, provides concluding comments, which point to the advantages of another taxonomy that hold the promise of improving the state of the current descriptive systems.

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20

Lang, Birgit. Erich Wulffen and the case of the criminal. Manchester University Press, 2017. http://dx.doi.org/10.7228/manchester/9780719099434.003.0005.

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State Prosecutor and legal reformer Erich Wulffen used the case study genre for legal and largely didactic purposes. Chapter 4 illustrates the adoption of the conventions of sexological case writing by the legal fraternity in twentieth-century Central Europe, and ways in which Wulffen brought the case study genre from the hidden world of the court to the wider public. In doing this, Wulffen carved a niche for himself as an expert in legal reform and sexology in Wilhelmine and Weimar Germany. He embraced different kinds of case modalities over the course of his professional career, targeting professional, middle-class audiences and the wider reading public during his thirty years in the role of prosecutor. The changing success of Wulffen’s publications highlights the intensifying crisis of the expert case study as a modality able to ‘speak the truth’ about modern sexuality and deviance. While Wulffen’s expert case studies about con men and other criminals were highly successful during the Wilhelmine era, the same approach and model for case writing met a more critical audience after 1918. Wulffen embraced the challenge of a new democratic environment by writing implicitly didactical popular crime novels. However, eventually his criminal subjects literally ‘wrote back’ after their sensationalised trials, using case studies in an attempt to narrate their own versions of events. The accounts of these criminals-turned-writers such as convicted paedophile Edith Cadivec. Thus the popularisation of sensationalist case studies, written, for instance, by perpetrators of crime, was an important factor in the case study genre’s loss of respectability.

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