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  Browse All Reviews > Mathematics Of Computing (G) > Probability And Statistics (G.3) > Markov Processes (G.3...)  
 
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  1-10 of 102 Reviews about "Markov Processes (G.3...)": Date Reviewed
  Reasoning with probabilistic and deterministic graphical models: exact algorithms (2nd ed.)
Dechter R.,  Morgan&Claypool Publishers, San Rafael, CA, 2019. 200 pp. Type: Book (978-1-681734-90-3)

Many problems related to learning and reasoning can make use of graphical models where a knowledge structure is compactly encoded into a graph. When dependencies (or independencies) among the variables of concern are effectively captured in graphi...

May 4 2021
  Interactive recommendation with user-specific deep reinforcement learning
Lei Y., Li W.  ACM Transactions on Knowledge Discovery from Data 13(6): 1-15, 2019. Type: Article

Recommender systems are widely used, especially by online applications with a view to enhancing user experience. In most conventional systems, past history of a user’s implicit online behavior is used to derive a new recommendation. By enabl...

Mar 2 2020
  Unifying logical and statistical AI with Markov logic
Domingos P., Lowd D.  Communications of the ACM 62(7): 74-83, 2019. Type: Article

A Markov logic network (MLN) is a probabilistic logic that endows the first-order logic with a degree of uncertainty by applying Markov networks. It represents an active area of research, introduced in the early 2000s by the very authors of the pr...

Sep 20 2019
  Answer set programming for non-stationary Markov decision processes
Ferreira L., Bianchi R., Santos P., Lopez de Mantaras R.  Applied Intelligence 47(4): 993-1007, 2017. Type: Article

Problem solving with computers often involves the exploration of paths from an initial state to a goal state. In addition to the size of this search space, there are many factors complicating this approach, especially in realistic environments. In...

Mar 13 2018
  Analyzing sentiments in one go: a supervised joint topic modeling approach
Hai Z., Cong G., Chang K., Cheng P., Miao C.  IEEE Transactions on Knowledge and Data Engineering 29(6): 1172-1185, 2017. Type: Article

The so-called “crowd wisdom” phenomenon took on a whole new dimension with the advent of the web and the emergence of myriads of online product reviews written by both happy and (more often than not) angry customers. A thorough underst...

Jan 17 2018
   Markov chains and Markov decision processes in Isabelle/HOL
Hölzl J.  Journal of Automated Reasoning 59(3): 345-387, 2017. Type: Article

The intermingling of rather different domains can, at times, produce rather interesting results. Here the author explores the intersection of probability theory (in the guise of Markov chains and Markov decision processes) and formal proof (throug...

Jan 11 2018
  MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data
Becker M., Lemmerich F., Singer P., Strohmaier M., Hotho A.  Data Mining and Knowledge Discovery 31(5): 1359-1390, 2017. Type: Article

This well-written paper includes adequate definitions to enable a layperson to understand the principles (generative processes of heterogeneous sequence data of human movement in a city) examined in its simulated experimental study. It uses approp...

Dec 7 2017
  Temporal probabilistic measure for link prediction in collaborative networks
Jaya Lakshmi T., Durga Bhavani S.  Applied Intelligence 47(1): 83-95, 2017. Type: Article

Research on social networks is a fashionable field, along with forecasting the behaviors of entities that are represented as nodes in a graph describing the relationship between entities. The authors investigate opportunities for the improvement o...

Sep 12 2017
  Brain tumor segmentation from multimodal magnetic resonance images via sparse representation
Li Y., Jia F., Qin J.  Artificial Intelligence in Medicine 731-13, 2016. Type: Article

The segmentation of brain tumors in magnetic resonance imaging (MRI) is clearly not only an important image processing task, but one in which the achievement of high accuracy can be life-changing for many. Although MRIs are performed in a number o...

Jul 13 2017
  Markov chain aggregation for agent-based models
Banisch S.,  Springer International Publishing, New York, NY, 2015. 195 pp. Type: Book (978-3-319248-75-2)

Most techniques for modeling dynamic systems fall into one of two categories. Equation-based models such as system dynamics and other differential equation formalisms seek a closed-form expression for the overall dynamics, but typically characteri...

Jul 12 2017
 
 
 
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