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Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11807 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
Kinetic algorithms via selfadjusting computation
 In Proceedings of the 14th Annual European Symposium on Algorithms (ESA 2006
, 2006
"... Abstract. Define a static algorithm as an algorithm that computes some combinatorial property of its input consisting of static, i.e., nonmoving, objects. In this paper, we describe a technique for syntactically transforming static algorithms into kinetic algorithms, which compute properties of mov ..."
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Cited by 12 (9 self)
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Abstract. Define a static algorithm as an algorithm that computes some combinatorial property of its input consisting of static, i.e., nonmoving, objects. In this paper, we describe a technique for syntactically transforming static algorithms into kinetic algorithms, which compute properties
Kinetic Algorithms via SelfAdjusting Computation
, 2006
"... Define a static algorithm to be an algorithm that computes some combinatorial property of its input consisting of static, i.e., nonmoving, objects. In this paper, we describe a technique for syntactically transforming static algorithms into kinetic algorithms, which compute the statically computed ..."
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Define a static algorithm to be an algorithm that computes some combinatorial property of its input consisting of static, i.e., nonmoving, objects. In this paper, we describe a technique for syntactically transforming static algorithms into kinetic algorithms, which compute the statically computed
Selfadjusting binary search trees
, 1985
"... The splay tree, a selfadjusting form of binary search tree, is developed and analyzed. The binary search tree is a data structure for representing tables and lists so that accessing, inserting, and deleting items is easy. On an nnode splay tree, all the standard search tree operations have an am ..."
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Cited by 435 (19 self)
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The splay tree, a selfadjusting form of binary search tree, is developed and analyzed. The binary search tree is a data structure for representing tables and lists so that accessing, inserting, and deleting items is easy. On an nnode splay tree, all the standard search tree operations have
SelfAdjusting Computation
 In ACM SIGPLAN Workshop on ML
, 2005
"... From the algorithmic perspective, we describe novel data structures for tracking the dependences ina computation and a changepropagation algorithm for adjusting computations to changes. We show that the overhead of our dependence tracking techniques is O(1). To determine the effectiveness of change ..."
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Cited by 49 (19 self)
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From the algorithmic perspective, we describe novel data structures for tracking the dependences ina computation and a changepropagation algorithm for adjusting computations to changes. We show that the overhead of our dependence tracking techniques is O(1). To determine the effectiveness
Programmable SelfAdjusting Computation
, 2010
"... and by donations from Intel Corporation. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: se ..."
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Cited by 2 (0 self)
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: selfadjusting computation, adaptivity, memoization, changepropagation, continuationpassing style, typed compilation, cost semantics, trace distance, traceable Selfadjusting computation is a paradigm for programming incremental computations that efficiently respond to input changes by updating
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
SelfAdjusting Programming
, 2005
"... This papers proposes techniques for writing selfadjusting programs that can adjust to any change to their data (e.g., inputs, decisions made during the computation) etc. We show that the techniques are e#cient by considering a number of applications including several sorting algorithms, and the Gra ..."
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, and the Graham Scan, and the quick hull algorithm for computing convex hulls. We show that the techniques are flexible by showing that selfadjusting programs can be trivially transformed into a kinetic programs that maintain their property as their input move continuously. We show that the techniques
Published In SelfAdjusting Programming
"... This papers proposes techniques for writing selfadjusting programs that can adjust to any change to their data (e.g., inputs, decisions made during the computation) etc. We show that the techniques are efficient by considering a number of applications including several sorting algorithms, and the ..."
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, and the Graham Scan, and the quick hull algorithm for computing convex hulls. We show that the techniques are flexible by showing that selfadjusting programs can be trivially transformed into a kinetic programs that maintain their property as their input move continuously. We show that the techniques
Randomized Algorithms
, 1995
"... Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simp ..."
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Cited by 2210 (37 self)
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Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available
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