Dilation for Sets of Probabilities
... We begin with precise beliefs about the second toss and then, no matter what happens on the first toss, merely learning that the first toss has occurred causes our beliefs about the second toss to become completely vacuous. The important point is that this phenomenon occurs no matter what the outcom ...
... We begin with precise beliefs about the second toss and then, no matter what happens on the first toss, merely learning that the first toss has occurred causes our beliefs about the second toss to become completely vacuous. The important point is that this phenomenon occurs no matter what the outcom ...
Aalborg Universitet Trigonometric quasi-greedy bases for Lp(T;w) Nielsen, Morten
... where h·, ·i is the standard inner product on L2 (T). Thus, the greedy algorithm for T in Lp (T; w) coincides with the usual greedy algorithm for the trigonometric system. Our main result in Section 3 gives a complete characterization of the non-negative weights w on T := [−π, π) such that T forms a ...
... where h·, ·i is the standard inner product on L2 (T). Thus, the greedy algorithm for T in Lp (T; w) coincides with the usual greedy algorithm for the trigonometric system. Our main result in Section 3 gives a complete characterization of the non-negative weights w on T := [−π, π) such that T forms a ...
Distinguishing Hidden Markov Chains
... Hidden Markov Chains (HMCs) are commonly used mathematical models of probabilistic systems. They are specified by a Markov Chain, capturing the probabilistic behavior of a system, and an observation function specifying the outputs generated from each of its states. Figure 1 depicts two example HMCs ...
... Hidden Markov Chains (HMCs) are commonly used mathematical models of probabilistic systems. They are specified by a Markov Chain, capturing the probabilistic behavior of a system, and an observation function specifying the outputs generated from each of its states. Figure 1 depicts two example HMCs ...
Recursive Markov Chains, Stochastic Grammars, and Monotone
... of basic algorithmic questions about them have not been satisfactorily answered. For example, is the probability of the language of a given SCFG or the extinction probability of a MT-BP ≥ p? Is it = 1? Can these questions be decided in polynomial-time in general? What if there are only a constant nu ...
... of basic algorithmic questions about them have not been satisfactorily answered. For example, is the probability of the language of a given SCFG or the extinction probability of a MT-BP ≥ p? Is it = 1? Can these questions be decided in polynomial-time in general? What if there are only a constant nu ...
The Laws of Probability and the Law of the Land
... ,1Cohen's arguments as to the difficulty of quantifying the burden of persuasion in civil cases are considered in more detail in Kaye, The Paradox of the Gatecrasherand Other Stories, 1979 ARIz. ST. L.J. 101. The analysis in Lempert, supra note 6, implicitly helps resolve some of the other "anomalie ...
... ,1Cohen's arguments as to the difficulty of quantifying the burden of persuasion in civil cases are considered in more detail in Kaye, The Paradox of the Gatecrasherand Other Stories, 1979 ARIz. ST. L.J. 101. The analysis in Lempert, supra note 6, implicitly helps resolve some of the other "anomalie ...
An Evaluation of Microsoft Word 97’s Grammar Checker
... The publishers of the early grammar and style software made extravagant claims about their products’ performance, claims that were, in my experience, unfounded. These days, grammar checkers, although still far from perfect, are much better and easier to use as well. In fact, it’s hard to ignore them ...
... The publishers of the early grammar and style software made extravagant claims about their products’ performance, claims that were, in my experience, unfounded. These days, grammar checkers, although still far from perfect, are much better and easier to use as well. In fact, it’s hard to ignore them ...
When Do Type Structures Contain All Hierarchies of Beliefs?*
... because Ann’s map β a is not surjective. Of course, in our example, it cannot be surjective since T a contains only a single point. So, imagine that we expand T a so that it is now the set of probability measures on {Heads, T ails} × T b and take β a to be the identity map. Then, β a is surjective, ...
... because Ann’s map β a is not surjective. Of course, in our example, it cannot be surjective since T a contains only a single point. So, imagine that we expand T a so that it is now the set of probability measures on {Heads, T ails} × T b and take β a to be the identity map. Then, β a is surjective, ...
Time Series Prediction and Online Learning
... restrictive distributional assumptions. Drifting or tracking scenarios extend the classical setting to non-stationary sequences of independent random variables (Ben-David et al., 1989; Bartlett, 1992; Barve and Long, 1997; Even-Dar et al., 2010; Mohri and Muñoz Medina, 2012). The scenario of learni ...
... restrictive distributional assumptions. Drifting or tracking scenarios extend the classical setting to non-stationary sequences of independent random variables (Ben-David et al., 1989; Bartlett, 1992; Barve and Long, 1997; Even-Dar et al., 2010; Mohri and Muñoz Medina, 2012). The scenario of learni ...
Streaming algorithms for embedding and computing edit distance in
... for computing the edit distance of a string of parenthesis of various types from the set of well parenthesized expressions. The main idea of the algorithm presented in [Sah14] is to process the string left to right, push opening parenthesis on a stack and match them against closing parenthesis. When ...
... for computing the edit distance of a string of parenthesis of various types from the set of well parenthesized expressions. The main idea of the algorithm presented in [Sah14] is to process the string left to right, push opening parenthesis on a stack and match them against closing parenthesis. When ...
Range-Efficient Counting of Distinct Elements in a Massive Data
... 1.1. Our results. We consider the problem of range-efficient computation of F0 , defined as follows. The input stream is R = r1 , r2 , . . . , rm , where each stream element ri = [xi , yi ] ⊂ [1, n] is an interval of integers xi , xi + 1, . . . , yi . The length of an interval ri could be any number be ...
... 1.1. Our results. We consider the problem of range-efficient computation of F0 , defined as follows. The input stream is R = r1 , r2 , . . . , rm , where each stream element ri = [xi , yi ] ⊂ [1, n] is an interval of integers xi , xi + 1, . . . , yi . The length of an interval ri could be any number be ...
