Applying Markov Decision Processes to Role-playing Game
|
|
|
- Σωστράτη Δημαράς
- 5 χρόνια πριν
- Προβολές:
Transcript
1 1,a) , MDPRPG RPG MDP RPG MDP RPG MDP RPG MDP RPG Applying Markov Decision Processes to Role-playing Game Yasunari Maeda 1,a) Fumitaro Goto 1 Hiroshi Masui 1 Fumito Masui 1 Masakiyo Suzuki 1 Received: August 25, 2011, Accepted: March 2, 2012 Abstract: In previous research a part of role-playing game (RPG) is represented with Markov decision processes (MDP). In this research we represent a more general PRG with MDP. We maximize an expected total reward under the condition that the true parameter of MDP is known in the first proposition. We maximize an expected total reward with respect to a Bayes criterion under the condition that the true parameter of MDP is unknown in the second proposition. We approximately maximize an expected total reward using learning data under the condition that the true parameter of MDP is unknown in the third proposition. Keywords: Markov decision processes, role-playing game, statistical decision theory, dynamic programming, Bayes criterion 1. RPG RPG [1], [2] RPG RPG 1 Kitami Institute of Technology, Kitami, Hokkaido , Japan a) maeda@cs.kitami-it.ac.jp [1], [2] MDP [3], [4], [5] RPG MDP DP [1], [2] RPG MDP RPG c 2012 Information Processing Society of Japan 1608
2 1 MDP RPG 2. MDP RPG 2.1 RPG RPG HP HP 0 HP M hp sm i SM SM = {sm 1,sm 2,...,sm SM } sm 1 f i F F = {f 1,f 2,...,f F } F (sm i ) F e i E E = {e 1,e 2,...,e E } M(e i ) e i e i HP HP 0 G(e i ) a 1 a 4 a 5 a 6 a 1 a 2 a 3 a 4 mv(sm i,a j ) sm i a j p(e k F (mv(sm i,a j )),θ ) mv(sm i,a j ) e k 1 e k E p(e k F (mv(sm i,a j )),θ ) a 5 p(c(e i ) a 5,e i,θ ) e i e i HP C(e i ) 1 p(c(e i ) a 5,e i,θ ) e i a 5 p(b(e i ) e i,θ ) e i HP B(e i ) e i 1 p(b(e i ) e i,θ ) a 6 p(map a 6,θ ) 1 p(map a 6,θ ) a 6 θ 2.2 MDP RPG MDP MDP s i s i S S = {s 1,s 2,...,s S } S a i a i A A = {a 1,a 2,...,a A } A s i a j s k p(s k s i,a j,ξ ) ξ r(s i,a j,s k ) 1 2 MDP MDP DP ξ (1) t s i t V (s i,t) V (s i,t) =max p(s k s i,a j,ξ )(r(s i,a j,s k )+V(s k,t+1)). a j A s k S (1) MDP RPG x t MDP t x t =(x t,1,x t,2,x t,3,x t,4 ), (2) x t,1 t HP x t,2 t x t,3 t 1 MDP Fig. 1 An example of MDP. c 2012 Information Processing Society of Japan 1609
3 x t,4 t HP x t,3 = x t,4 =0 A(x t ) x t MDP y t MDP t t x t y t t +1 p(e i F (mv(x t,2,y t )),θ ) e i x t+1 =(x t,1,mv(x t,2,y t ),e i,m(e i )), (3) sm 1 mv(x t,2,y t ) p(e i F (mv(x t,2,y t )),θ ) = 0 1 e p(e i E i F (mv(x t,2,y t )),θ ) x t+1 x t+1 mv(x t,2,y t ) sm 1 =(M hp,sm 1,x t,3,x t,4 ), (4) mv(x t,2,y t ) sm 1 =(x t,1,mv(x t,2,y t ),x t,3,x t,4 ), (5) (4) sm 1 HP M hp t x t y t y t a 5 a 6 a 5 (1 p(c(x t,3 ) a 5,x t,3,θ ))(1 p(b(x t,3 ) x t,3,θ )) x t+1 =(x t,1,x t,2,x t,3,x t,4 ), (6) (1 p(c(x t,3 ) a 5,x t,3,θ ))p(b(x t,3 ) x t,3,θ ) x t+1 x t+1 x t,1 >B(x t,3 ) =(x t,1 B(x t,3 ),x t,2,x t,3,x t,4 ), (7) x t,1 B(x t,3 ) =(M hp,sm 1, 0, 0), (8) (8) sm 1 p(c(x t,3 ) a 5,x t,3,θ )(1 p(b(x t,3 ) x t,3,θ )) x t+1 x t+1 x t,4 >C(x t,3 ) =(x t,1,x t,2,x t,3,x t,4 C(x t,3 )), (9) x t,4 C(x t,3 ) =(x t,1,x t,2, 0, 0), (10) (10) x t,3 r(x t,a 5,x t+1 ) = G(x t,3 ) p(c(x t,3 ) a 5,x t,3,θ )p(b(x t,3 ) x t,3,θ ) x t+1 x t+1 x t,4 C(x t,3 ) =(x t,1,x t,2, 0, 0), (11) x t,4 >C(x t,3 ) x t,1 >B(x t,3 ) =(x t,1 B(x t,3 ),x t,2,x t,3,x t,4 C(x t,3 )), (12) x t,4 >C(x t,3 ) x t,1 B(x t,3 ) =(M hp,sm 1, 0, 0), (13) (11) x t,3 r(x t,a 5,x t+1 )= G(x t,3 ) (13) sm 1 t x t a 6 p(map a 6,θ ) x t+1 =(x t,1,x t,2, 0, 0), (14) (1 p(map a 6,θ ))(1 p(b(x t,3 ) x t,3,θ )) x t+1 =(x t,1,x t,2,x t,3,x t,4 ), (15) (1 p(map a 6,θ ))p(b(x t,3 ) x t,3,θ ) c 2012 Information Processing Society of Japan 1610
4 x t+1 x t+1 x t,1 >B(x t,3 ) =(x t,1 B(x t,3 ),x t,2,x t,3,x t,4 ), (16) x t,1 B(x t,3 ) =(M hp,sm 1, 0, 0), (17) (17) sm 1 x t,3 r(x t,a 5,x t+1 )=G(x t,3 ) r(x t,a 5,x t+1 )=0 x 1 x 1 =(M hp,sm 1, 0, 0) C(e i ) B(e i ) G(e i ) T T t=1 r(x t,y t,x t+1 ) 3. [6], [7] 3.1 θ U(d(, ),x T +1 y T,θ ) U(d(, ),x T +1 y T,θ )= T r(x t,y t,x t+1 ), (18) t=1 d(, ) x T +1 y T x 1 y 1 x 2 y 2 x T y T x T +1 U(d(, ),x T +1 y T,θ ) θ d(, ) x T +1 y T EU(d(, ),θ ) EU(d(, ),θ ) ( T ) = E r(x t,y t,x t+1 ) t=1 = p(x 2 x 1,y 1,θ )(r(x 1,y 1,x 2 ) x T y T +p(x 3 x 2,y 2,θ )(r(x 2,y 2,x 3 ) + + p(x T +1 x T,y T,θ )r(x T,y T,x T +1 )) )), (19) EU(d(, ),θ ) θ d(, ) θ d (, ) = arg max d(, ) EU(d(, ),θ ). (20) DP (20) DP T MDP T T x T T d (x T,T) =arg V (x T,T) = max max y T A(x T ) y T A(x T ) x T +1 p(x T +1 x T,y T,θ )r(x T,y T,x T +1 ), (21) x T +1 p(x T +1 x T,y T,θ )r(x T,y T,x T +1 ), (22) d (x T,T) T x T V (x T,T) T x T T +1 1 t 1 t T 1 x t t d (x t,t) =arg max p(x t+1 x t,y t,θ )(r(x t,y t,x t+1 ) y t A(x t) x t+1 +V (x t+1,t+1)), (23) V (x t,t)= max p(x t+1 x t,y t,θ )(r(x t,y t,x t+1 ) y t A(x t) x t+1 + V (x t+1,t+1)), (24) d (x t,t) t x t V (x t,t) t x t t (21) (24) d (x 1, 1) 1 T c 2012 Information Processing Society of Japan 1611
5 3.2 DP θ p(θ) θ θ θ x t y t 1 t x t x t y t 1 = x 1 y 1 x t BEU(d B (,, ),p(θ))= p(θ)eu(d B (,, ),θ)dθ, (25) θ d B(,, ) = arg max d B(,, ) BEU(d B(,, ),p(θ)). (26) DP DP T DP T 1 T x T x T y T 1 d B(x T,x T y T 1,T) =arg max y T A(x T ) x T +1 p(θ x T y T 1 )p(x T +1 x T,y T,θ)dθ r(x T,y T,x T +1 ), (27) V B (x T,x T y T 1,T) = max p(θ x T y T 1 )p(x T +1 x T,y T,θ)dθ y T A(x T ) x T +1 r(x T,y T,x T +1 ), (28) p(θ x T y T 1 ) 1 T x T y T 1 t 1 t T 1 x t x t y t 1 d B(x t,x t y t 1,t) =arg max y t A(x t) x t+1 p(θ x t y t 1 )p(x t+1 x t,y t,θ)dθ (r(x t,y t,x t+1 )+V B (x t+1,x t+1 y t,t+1)). (29) V B (x t,x t y t 1,t) = max p(θ x t y t 1 )p(x t+1 x t,y t,θ)dθ y t A(x t) x t+1 (r(x t,y t,x t+1 )+V B (x t+1,x t+1 y t,t+1)). (30) (27) (30) d B (x 1,x 1, 1) 1 T (27) (30) [8] t x t y t e i x t+1 p(θ xt y t 1 )p(x t+1 x t,y t,θ)dθ p(θ x t y t 1 )p(x t+1 x t,y t,θ)dθ = p(θ x t y t 1 )p(e i F (mv(x t,2,y t )),θ)dθ = N(F (mv(x t,2,y t )),e i x t y t 1 )+α 1 N(F (mv(x t,2,y t )) x t y t 1 )+α 2, (31) α 1 = α(e i F (mv(x t,2,y t ))), (32) α 2 = α(e j F (mv(x t,2,y t ))) e j E + α(mv(x t,2,y t ) F (mv(x t,2,y t ))), (33) N(F (mv(x t,2,y t )),e i x t y t 1 ) x t y t 1 F (mv(x t,2,y t )) e i N(F (mv(x t,2,y t )) x t y t 1 ) x t y t 1 F (mv(x t,2,y t )) α(e i F (mv(x t,2,y t ))) p(e i F (mv(x t,2,y t )),θ) α(mv(x t,2,y t ) F (mv(x t,2,y t ))) 1 e p(e i E i F (mv(x t,2,y t )),θ) c 2012 Information Processing Society of Japan 1612
6 [6], [7], [8], [9] 0.5 (27) (30) MDP MDP [10] [10] RPG 3.3 DP (24) DP (30) t t L x t y t 1 p(θ xt y t 1 )p(x t+1 x t,y t,θ)dθ L p(θ L)p(x t+1 x t,y t,θ)dθ p(θ L)p(x t+1 x t,y t,θ)dθ (21) (24) DP HP 6 sm 4 a 3 sm 7 HP a 1 HP a 4 3 HP 1 sm 4 a 4 HP 2 Fig. 2 A map of experiment. 2 1 Table 2 Settings of probabilities (the first part). p(e 1 f 2,θ ) p(e 2 f 2,θ ) p(e 1 f 3,θ ) p(e 2 f 3,θ ) Table 1 Settings of the ground. F(sm 1 ) F(sm 2 ) F(sm 3 ) F(sm 4 ) F(sm 5 ) F(sm 6 ) F(sm 7 ) F(sm 8 ) F(sm 9 ) f 1 f 2 f 3 f 2 f 2 f 3 f 3 f 3 f 3 c 2012 Information Processing Society of Japan 1613
7 Table Settings of probabilities (the second part). p(c(e 1 ) a 5,e 1,θ ) p(c(e 2 ) a 5,e 2,θ ) p(b(e 1 ) e 1,θ ) p(b(e 2 ) e 2,θ ) p(map a 6,θ ) Table 4 Othe settings (the first part). Mhp E M(e 1 ) M(e 2 ) G(e 1 ) G(e 2 ) 10 {e 1,e 2 } Table 5 Othe settings (the second part). C(e 1 ) C(e 2 ) B(e 1 ) B(e 2 ) T sm 1 HP 9 HP 1 sm 4 a 1 sm 5 HP sm , % % 1,000 96% DP 5. RPG MDP RPG MDP RPG MDP RPG MDP RPG RPG MDP RPG MDP [11], [12], [13], [14] RPG DP MDP MDP POMDP [15], [16] RPG POMDP RPG MDP RPG RPG c 2012 Information Processing Society of Japan 1614
![RPG [1] RPG GI, Vol.2001, No.28, pp.31 38 (2001). [2] RPG GI, Vol.2001, No.58, pp.67 74 (2001). [3] (1973).](/docs-images/93/114099010/images/8-0.jpg "[4] Martin, L.P.: Markov Decision Processes, John Wiley & Sons (1994). [5] (1979). [6] Berger, J.O.")
![: Statistical Decision Theory and Bayesian Analysis, Springer-Verlag, New York (1980). [7] (1985).](/docs-images/93/114099010/images/8-1.jpg "[8] Matsushima, T. and Hirasawa, S.")
![: A Bayes coding algorithm for Markov models, TECHNICAL REPORT OF IEICE, IT95-1, pp.1 6 (1995). [9] (2009).](/docs-images/93/114099010/images/8-2.jpg "[10] Martin, J.J.: Bayesian Decision Problems and Markov Chains, John Wiley & Sons (1967). [11] Vol.39, No.")
![4, pp.1116 1126 (1998). [12] k- Vol.10, No.3, pp.454 463 (1995). [13] l- Vol.11, No.5, pp.804 808 (1996).](/docs-images/93/114099010/images/8-3.jpg "[14] MarcoPolo Vol.12, No.1, pp.78 89 (1997). [15] Kaelbling, L.P. Vol.12, No.6, pp.822 830 (1997).")
8 RPG [1] RPG GI, Vol.2001, No.28, pp (2001). [2] RPG GI, Vol.2001, No.58, pp (2001). [3] (1973). [4] Martin, L.P.: Markov Decision Processes, John Wiley & Sons (1994). [5] (1979). [6] Berger, J.O.: Statistical Decision Theory and Bayesian Analysis, Springer-Verlag, New York (1980). [7] (1985). [8] Matsushima, T. and Hirasawa, S.: A Bayes coding algorithm for Markov models, TECHNICAL REPORT OF IEICE, IT95-1, pp.1 6 (1995). [9] (2009). [10] Martin, J.J.: Bayesian Decision Problems and Markov Chains, John Wiley & Sons (1967). [11] Vol.39, No.4, pp (1998). [12] k- Vol.10, No.3, pp (1995). [13] l- Vol.11, No.5, pp (1996). [14] MarcoPolo Vol.12, No.1, pp (1997). [15] Kaelbling, L.P. Vol.12, No.6, pp (1997). [16] POMDPs Vol.14, No.1, pp (1999) c 2012 Information Processing Society of Japan 1615
9 c 2012 Information Processing Society of Japan 1616
: Monte Carlo EM 313, Louis (1982) EM, EM Newton-Raphson, /. EM, 2 Monte Carlo EM Newton-Raphson, Monte Carlo EM, Monte Carlo EM, /. 3, Monte Carlo EM
2008 6 Chinese Journal of Applied Probability and Statistics Vol.24 No.3 Jun. 2008 Monte Carlo EM 1,2 ( 1,, 200241; 2,, 310018) EM, E,,. Monte Carlo EM, EM E Monte Carlo,. EM, Monte Carlo EM,,,,. Newton-Raphson.
Buried Markov Model Pairwise
Buried Markov Model 1 2 2 HMM Buried Markov Model J. Bilmes Buried Markov Model Pairwise 0.6 0.6 1.3 Structuring Model for Speech Recognition using Buried Markov Model Takayuki Yamamoto, 1 Tetsuya Takiguchi
ΕΚΠ 413 / ΕΚΠ 606 Αυτόνοµοι (Ροµ οτικοί) Πράκτορες
ΕΚΠ 413 / ΕΚΠ 606 Αυτόνοµοι (Ροµ οτικοί) Πράκτορες Λήψη Α οφάσεων υ ό Αβεβαιότητα Decision Making under Uncertainty Τµήµα Ηλεκτρονικών Μηχανικών και Μηχανικών Υ ολογιστών Πολυτεχνείο Κρήτης Ε ανάληψη Εντο
EM Baum-Welch. Step by Step the Baum-Welch Algorithm and its Application 2. HMM Baum-Welch. Baum-Welch. Baum-Welch Baum-Welch.
Baum-Welch Step by Step the Baum-Welch Algorithm and its Application Jin ichi MURAKAMI EM EM EM Baum-Welch Baum-Welch Baum-Welch Baum-Welch, EM 1. EM 2. HMM EM (Expectationmaximization algorithm) 1 3.
GPGPU. Grover. On Large Scale Simulation of Grover s Algorithm by Using GPGPU
GPGPU Grover 1, 2 1 3 4 Grover Grover OpenMP GPGPU Grover qubit OpenMP GPGPU, 1.47 qubit On Large Scale Simulation of Grover s Algorithm by Using GPGPU Hiroshi Shibata, 1, 2 Tomoya Suzuki, 1 Seiya Okubo
Stabilization of stock price prediction by cross entropy optimization
,,,,,,,, Stabilization of stock prediction by cross entropy optimization Kazuki Miura, Hideitsu Hino and Noboru Murata Prediction of series data is a long standing important problem Especially, prediction
Feasible Regions Defined by Stability Constraints Based on the Argument Principle
Feasible Regions Defined by Stability Constraints Based on the Argument Principle Ken KOUNO Masahide ABE Masayuki KAWAMATA Department of Electronic Engineering, Graduate School of Engineering, Tohoku University
Bayesian statistics. DS GA 1002 Probability and Statistics for Data Science.
Bayesian statistics DS GA 1002 Probability and Statistics for Data Science http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall17 Carlos Fernandez-Granda Frequentist vs Bayesian statistics In frequentist
Simplex Crossover for Real-coded Genetic Algolithms
Technical Papers GA Simplex Crossover for Real-coded Genetic Algolithms 47 Takahide Higuchi Shigeyoshi Tsutsui Masayuki Yamamura Interdisciplinary Graduate school of Science and Engineering, Tokyo Institute
ST5224: Advanced Statistical Theory II
ST5224: Advanced Statistical Theory II 2014/2015: Semester II Tutorial 7 1. Let X be a sample from a population P and consider testing hypotheses H 0 : P = P 0 versus H 1 : P = P 1, where P j is a known
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr T t N n) Pr max X 1,..., X N ) t N n) Pr max
Η Επίδραση των Events στην Απόδοση των Μετοχών
Χρηματοοικονομικά και Διοίκηση Μεταπτυχιακή διατριβή Η Επίδραση των Events στην Απόδοση των Μετοχών Άντρεα Φωτίου Λεμεσός, Μάιος 2018 ΤΕΧΝΟΛΟΓΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ ΣΧΟΛΗ ΧΡΗΜΑΤΟΟΙΚΟΝΟΜΙΚΩΝ ΚΑΙ ΔΙΟΙΚΗΣΗΣ
Second Order Partial Differential Equations
Chapter 7 Second Order Partial Differential Equations 7.1 Introduction A second order linear PDE in two independent variables (x, y Ω can be written as A(x, y u x + B(x, y u xy + C(x, y u u u + D(x, y
A Bonus-Malus System as a Markov Set-Chain. Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics
A Bonus-Malus System as a Markov Set-Chain Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics Contents 1. Markov set-chain 2. Model of bonus-malus system 3. Example 4. Conclusions
6. MAXIMUM LIKELIHOOD ESTIMATION
6 MAXIMUM LIKELIHOOD ESIMAION [1] Maximum Likelihood Estimator (1) Cases in which θ (unknown parameter) is scalar Notational Clarification: From now on, we denote the true value of θ as θ o hen, view θ
Maxima SCORM. Algebraic Manipulations and Visualizing Graphs in SCORM contents by Maxima and Mashup Approach. Jia Yunpeng, 1 Takayuki Nagai, 2, 1
Maxima SCORM 1 2, 1 Muhammad Wannous 1 3, 4 2, 4 Maxima Web LMS MathML HTML5 Flot jquery JSONP JavaScript SCORM SCORM Algebraic Manipulations and Visualizing Graphs in SCORM contents by Maxima and Mashup
Homework 3 Solutions
Homework 3 Solutions Igor Yanovsky (Math 151A TA) Problem 1: Compute the absolute error and relative error in approximations of p by p. (Use calculator!) a) p π, p 22/7; b) p π, p 3.141. Solution: For
GPU. CUDA GPU GeForce GTX 580 GPU 2.67GHz Intel Core 2 Duo CPU E7300 CUDA. Parallelizing the Number Partitioning Problem for GPUs
GPU 1 1 NP number partitioning problem Pedroso CUDA GPU GeForce GTX 580 GPU 2.67GHz Intel Core 2 Duo CPU E7300 CUDA C Pedroso Python 323 Python C 12.2 Parallelizing the Number Partitioning Problem for
Probabilistic Approach to Robust Optimization
Probabilistic Approach to Robust Optimization Akiko Takeda Department of Mathematical & Computing Sciences Graduate School of Information Science and Engineering Tokyo Institute of Technology Tokyo 52-8552,
* * E mail : matsuto eng.hokudai.ac.jp. Zeiss
400 Vol., No., pp., * * R.... * * Email : matsutoeng.hokudai.ac.jp Zeiss 401 Petts Becker Petts Petts Opaluch Joos. I L T M P TMP MP IM A IP B LM C TMP LM D LM B LM E * LP F * km TMP C TP E * TP G * 402...
y = f(x)+ffl x 2.2 x 2X f(x) x x p T (x) = 1 Z T exp( f(x)=t ) (2) x 1 exp Z T Z T = X x2x exp( f(x)=t ) (3) Z T T > 0 T 0 x p T (x) x f(x) (MAP = Max
2006 2006 Workshop on Information-Based Induction Sciences (IBIS2006) Osaka, Japan, October 31- November 2, 2006. [ ] Introduction to statistical models for populational optimization Λ Shotaro Akaho Abstract:
AΕΙ ΠΕΙΡΑΙΑ T.T. ΣΧΟΛΗ ΤΕΧΝΟΛΟΓΙΚΩΝ ΕΦΑΡΜΟΓΩΝ
AΕΙ ΠΕΙΡΑΙΑ T.T. ΣΧΟΛΗ ΤΕΧΝΟΛΟΓΙΚΩΝ ΕΦΑΡΜΟΓΩΝ ΤΜΗΜΑ ΜΗΧΑΝΙΚΩΝ ΗΛΕΚΤΡΟΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤΙΚΩΝ ΣΥΣΤΗΜΑΤΩΝ T.E. ΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ Διδασκαλία του Προγραμματισμού στο Γυμνάσιο με το Snap! Άννα Ε. Μολέ Εισηγητής:
Solution Series 9. i=1 x i and i=1 x i.
Lecturer: Prof. Dr. Mete SONER Coordinator: Yilin WANG Solution Series 9 Q1. Let α, β >, the p.d.f. of a beta distribution with parameters α and β is { Γ(α+β) Γ(α)Γ(β) f(x α, β) xα 1 (1 x) β 1 for < x
HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch:
HOMEWORK 4 Problem a For the fast loading case, we want to derive the relationship between P zz and λ z. We know that the nominal stress is expressed as: P zz = ψ λ z where λ z = λ λ z. Therefore, applying
Japanese Fuzzy String Matching in Cooking Recipes
1 Japanese Fuzzy String Matching in Cooking Recipes Michiko Yasukawa 1 In this paper, we propose Japanese fuzzy string matching in cooking recipes. Cooking recipes contain spelling variants for recipe
VBA Microsoft Excel. J. Comput. Chem. Jpn., Vol. 5, No. 1, pp (2006)
J. Comput. Chem. Jpn., Vol. 5, No. 1, pp. 29 38 (2006) Microsoft Excel, 184-8588 2-24-16 e-mail: yosimura@cc.tuat.ac.jp (Received: July 28, 2005; Accepted for publication: October 24, 2005; Published on
IPSJ SIG Technical Report Vol.2014-CE-127 No /12/6 CS Activity 1,a) CS Computer Science Activity Activity Actvity Activity Dining Eight-He
CS Activity 1,a) 2 2 3 CS Computer Science Activity Activity Actvity Activity Dining Eight-Headed Dragon CS Unplugged Activity for Learning Scheduling Methods Hisao Fukuoka 1,a) Toru Watanabe 2 Makoto
CHAPTER 25 SOLVING EQUATIONS BY ITERATIVE METHODS
CHAPTER 5 SOLVING EQUATIONS BY ITERATIVE METHODS EXERCISE 104 Page 8 1. Find the positive root of the equation x + 3x 5 = 0, correct to 3 significant figures, using the method of bisection. Let f(x) =
J. of Math. (PRC) Banach, , X = N(T ) R(T + ), Y = R(T ) N(T + ). Vol. 37 ( 2017 ) No. 5
Vol. 37 ( 2017 ) No. 5 J. of Math. (PRC) 1,2, 1, 1 (1., 225002) (2., 225009) :. I +AT +, T + = T + (I +AT + ) 1, T +. Banach Hilbert Moore-Penrose.. : ; ; Moore-Penrose ; ; MR(2010) : 47L05; 46A32 : O177.2
MIA MONTE CARLO ΜΕΛΕΤΗ ΤΩΝ ΕΚΤΙΜΗΤΩΝ RIDGE ΚΑΙ ΕΛΑΧΙΣΤΩΝ ΤΕΤΡΑΓΩΝΩΝ
«ΣΠΟΥΔΑΙ», Τόμος 41, Τεύχος 2ο, Πανεπιστήμιο Πειραιώς «SPOUDAI», Vol. 41, No 2, University of Piraeus MIA MONTE CARLO ΜΕΛΕΤΗ ΤΩΝ ΕΚΤΙΜΗΤΩΝ RIDGE ΚΑΙ ΕΛΑΧΙΣΤΩΝ ΤΕΤΡΑΓΩΝΩΝ Του Πάνου Αναστ. Πανόπουλου Οικονομικό
Σχεδίαση και Ανάπτυξη Παιχνιδιού για την Εκμάθηση των Βασικών Στοιχείων ενός Υπολογιστή με Χρήση του Περιβάλλοντος GameMaker
Α Ρ Ι Σ Τ Ο Τ Ε Λ Ε Ι Ο Π Α Ν Ε Π Ι Σ Τ Η Μ Ι Ο Θ Ε Σ Σ Α Λ Ο Ν Ι Κ Η Σ ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ ΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ Ηλίας Ανδρεάδης Α.Ε.Μ 1443 Μιχάλης Στρατίδης Α.Ε.Μ 1543 Σχεδίαση και
SocialDict. A reading support tool with prediction capability and its extension to readability measurement
SocialDict 1 2 2 2 Web SocialDict A reading support tool with prediction capability and its extension to readability measurement Yo Ehara, 1 Takashi Ninomiya, 2 Nobuyuki Shimizu 2 and Hiroshi Nakagawa
Web-based supplementary materials for Bayesian Quantile Regression for Ordinal Longitudinal Data
Web-based supplementary materials for Bayesian Quantile Regression for Ordinal Longitudinal Data Rahim Alhamzawi, Haithem Taha Mohammad Ali Department of Statistics, College of Administration and Economics,
γ 1 6 M = 0.05 F M = 0.05 F M = 0.2 F M = 0.2 F M = 0.05 F M = 0.05 F M = 0.05 F M = 0.2 F M = 0.05 F 2 2 λ τ M = 6000 M = 10000 M = 15000 M = 6000 M = 10000 M = 15000 1 6 τ = 36 1 6 τ = 102 1 6 M = 5000
2. THEORY OF EQUATIONS. PREVIOUS EAMCET Bits.
EAMCET-. THEORY OF EQUATIONS PREVIOUS EAMCET Bits. Each of the roots of the equation x 6x + 6x 5= are increased by k so that the new transformed equation does not contain term. Then k =... - 4. - Sol.
Technical Research Report, Earthquake Research Institute, the University of Tokyo, No. +-, pp. 0 +3,,**1. No ,**1
No. +- 0 +3,**1 Technical Research Report, Earthquake Research Institute, the University of Tokyo, No. +-, pp. 0 +3,,**1. * Construction of the General Observation System for Strong Motion in Earthquake
Other Test Constructions: Likelihood Ratio & Bayes Tests
Other Test Constructions: Likelihood Ratio & Bayes Tests Side-Note: So far we have seen a few approaches for creating tests such as Neyman-Pearson Lemma ( most powerful tests of H 0 : θ = θ 0 vs H 1 :
ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΕΙΡΑΙΑ ΤΜΗΜΑ ΟΙΚΟΝΟΜΙΚΗΣ ΕΠΙΣΤΗΜΗΣ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΟΙΚΟΝΟΜΙΚΗ ΚΑΙ ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΣΤΡΑΤΗΓΙΚΗ ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ
ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΕΙΡΑΙΑ ΤΜΗΜΑ ΟΙΚΟΝΟΜΙΚΗΣ ΕΠΙΣΤΗΜΗΣ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΟΙΚΟΝΟΜΙΚΗ ΚΑΙ ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΣΤΡΑΤΗΓΙΚΗ ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ ΔΙΕΡΕΥΝΗΣΗ ΤΗΣ ΠΛΗΡΟΦΟΡΙΑΚΗΣ ΑΠΟΤΕΛΕΣΜΑΤΙΚΟΤΗΤΑΣ ΣΤΗΝ ΑΓΟΡΑ
[4] 1.2 [5] Bayesian Approach min-max min-max [6] UCB(Upper Confidence Bound ) UCT [7] [1] ( ) Amazons[8] Lines of Action(LOA)[4] Winands [4] 1
![[4] 1.2 [5] Bayesian Approach min-max min-max [6] UCB(Upper Confidence Bound ) UCT [7] [1] ( ) Amazons[8] Lines of Action(LOA)[4] Winands [4] 1](/thumbs/93/113995345.jpg "[4] 1.2 [5] Bayesian Approach min-max min-max [6] UCB(Upper Confidence Bound ) UCT [7] [1] ( ) Amazons[8] Lines of Action(LOA)[4] Winands [4] 1")
1,a) Bayesian Approach An Application of Monte-Carlo Tree Search Algorithm for Shogi Player Based on Bayesian Approach Daisaku Yokoyama 1,a) Abstract: Monte-Carlo Tree Search (MCTS) algorithm is quite
Resurvey of Possible Seismic Fissures in the Old-Edo River in Tokyo
Bull. Earthq. Res. Inst. Univ. Tokyo Vol. 2.,**3 pp.,,3,.* * +, -. +, -. Resurvey of Possible Seismic Fissures in the Old-Edo River in Tokyo Kunihiko Shimazaki *, Tsuyoshi Haraguchi, Takeo Ishibe +, -.
Problem Set 3: Solutions
CMPSCI 69GG Applied Information Theory Fall 006 Problem Set 3: Solutions. [Cover and Thomas 7.] a Define the following notation, C I p xx; Y max X; Y C I p xx; Ỹ max I X; Ỹ We would like to show that C
, Evaluation of a library against injection attacks
THE INSTITUTE OF ELECTRONICS, INFMATION AND COMMUNICATION ENGINEERS TECHNICAL REPT OF IEICE., () 211 8588 4 1 1 221 0835 2 14 1 E-mail: okubo@jp.fujitsu.com, tanaka@iisec.ac.jp Web,,,, Evaluation of a
Schedulability Analysis Algorithm for Timing Constraint Workflow Models
CIMS Vol.8No.72002pp.527-532 ( 100084) Petri Petri F270.7 A Schedulability Analysis Algorithm for Timing Constraint Workflow Models Li Huifang and Fan Yushun (Department of Automation, Tsinghua University,
Η Έννοια και η Σημασία της Τιμής
1 κ ε φ ά λ α ι ο Η Έννοια και η Σημασία της Τιμής 1.1 Εισαγωγή Το παρόν κεφάλαιο εξετάζει την έννοια της τιμής σε συνδυασμό με τη σημασία της τόσο για την ίδια την επιχείρηση όσο και γενικότερα για την
Bayesian modeling of inseparable space-time variation in disease risk
Bayesian modeling of inseparable space-time variation in disease risk Leonhard Knorr-Held Laina Mercer Department of Statistics UW May, 013 Motivation Ohio Lung Cancer Example Lung Cancer Mortality Rates
DEIM Forum 2018 F3-5 657 8501 1-1 657 8501 1-1 E-mail: yuta@cs25.scitec.kobe-u.ac.jp, eguchi@port.kobe-u.ac.jp, ( ) ( )..,,,.,.,.,,..,.,,, 2..., 1.,., (Autoencoder: AE) [1] (Generative Stochastic Networks:
GÖKHAN ÇUVALCIOĞLU, KRASSIMIR T. ATANASSOV, AND SINEM TARSUSLU(YILMAZ)
IFSCOM016 1 Proceeding Book No. 1 pp. 155-161 (016) ISBN: 978-975-6900-54-3 SOME RESULTS ON S α,β AND T α,β INTUITIONISTIC FUZZY MODAL OPERATORS GÖKHAN ÇUVALCIOĞLU, KRASSIMIR T. ATANASSOV, AND SINEM TARSUSLU(YILMAZ)
ΠΑΝΔΠΗΣΖΜΗΟ ΠΑΣΡΩΝ ΣΜΖΜΑ ΖΛΔΚΣΡΟΛΟΓΩΝ ΜΖΥΑΝΗΚΩΝ ΚΑΗ ΣΔΥΝΟΛΟΓΗΑ ΤΠΟΛΟΓΗΣΩΝ ΣΟΜΔΑ ΤΣΖΜΑΣΩΝ ΖΛΔΚΣΡΗΚΖ ΔΝΔΡΓΔΗΑ
ΠΑΝΔΠΗΣΖΜΗΟ ΠΑΣΡΩΝ ΣΜΖΜΑ ΖΛΔΚΣΡΟΛΟΓΩΝ ΜΖΥΑΝΗΚΩΝ ΚΑΗ ΣΔΥΝΟΛΟΓΗΑ ΤΠΟΛΟΓΗΣΩΝ ΣΟΜΔΑ ΤΣΖΜΑΣΩΝ ΖΛΔΚΣΡΗΚΖ ΔΝΔΡΓΔΗΑ Γηπισκαηηθή Δξγαζία ηνπ Φνηηεηή ηνπ ηκήκαηνο Ζιεθηξνιόγσλ Μεραληθώλ θαη Σερλνινγίαο Ζιεθηξνληθώλ
Research on Economics and Management
36 5 2015 5 Research on Economics and Management Vol. 36 No. 5 May 2015 490 490 F323. 9 A DOI:10.13502/j.cnki.issn1000-7636.2015.05.007 1000-7636 2015 05-0052 - 10 2008 836 70% 1. 2 2010 1 2 3 2015-03
Τι είναι η Θεωρία Παιγνίων;
Θεωρία Παιγνίων Τι είναι η Θεωρία Παιγνίων; Η ανάλυση ανταγωνιστικών (ή συγκρουσιακών) καταστάσεων με χρήση μαθηματικών μοντέλων Το πώς παίζεται το παίγνιο εξαρτάται από τη στρατηγική σχέδιο δράσης που
A Determination Method of Diffusion-Parameter Values in the Ion-Exchange Optical Waveguides in Soda-Lime glass Made by Diluted AgNO 3 with NaNO 3
大阪電気通信大学研究論集 ( 自然科学編 ) 第 51 号 A Determination Method of Diffusion-Parameter Values in the Ion-Exchange Optical Waveguides in Soda-Lime glass Made by Diluted AgNO 3 with NaNO 3 Takuya IWATA and Kiyoshi
1 n-gram n-gram n-gram [11], [15] n-best [16] n-gram. n-gram. 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e)
![1 n-gram n-gram n-gram [11], [15] n-best [16] n-gram. n-gram. 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e)](/thumbs/39/20145714.jpg "1 n-gram n-gram n-gram [11], [15] n-best [16] n-gram. n-gram. 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e)")
1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [11], [15] 1 Nara Institute of Science and Technology a) akabe.koichi.zx8@is.naist.jp b) neubig@is.naist.jp c) ssakti@is.naist.jp d) tomoki@is.naist.jp
n 1 n 3 choice node (shelf) choice node (rough group) choice node (representative candidate)
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. y y yy y 1565 0871 2 1 yy 525 8577 1 1 1 E-mail: yfmakihara,shiraig@cv.mech.eng.osaka-u.ac.jp, yyshimada@ci.ritsumei.ac.jp
ΓΙΑΝΝΟΥΛΑ Σ. ΦΛΩΡΟΥ Ι ΑΚΤΟΡΑΣ ΤΟΥ ΤΜΗΜΑΤΟΣ ΕΦΑΡΜΟΣΜΕΝΗΣ ΠΛΗΡΟΦΟΡΙΚΗΣ ΤΟΥ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΜΑΚΕ ΟΝΙΑΣ ΒΙΟΓΡΑΦΙΚΟ ΣΗΜΕΙΩΜΑ
ΓΙΑΝΝΟΥΛΑ Σ. ΦΛΩΡΟΥ Ι ΑΚΤΟΡΑΣ ΤΟΥ ΤΜΗΜΑΤΟΣ ΕΦΑΡΜΟΣΜΕΝΗΣ ΠΛΗΡΟΦΟΡΙΚΗΣ ΤΟΥ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΜΑΚΕ ΟΝΙΑΣ ΑΝΑΠΛΗΡΩΤΡΙΑ ΚΑΘΗΓΗΤΡΙΑ ΤΟΥ ΤΜΗΜΑΤΟΣ ΛΟΓΙΣΤΙΚΗΣ ΤΟΥ ΤΕΙ ΚΑΒΑΛΑΣ ΒΙΟΓΡΑΦΙΚΟ ΣΗΜΕΙΩΜΑ ΙΑΝΟΥΑΡΙΟΣ 2008 ΒΙΟΓΡΑΦΙΚΟ
page: 2 (2.1) n + 1 n {n} N 0, 1, 2
page: 1 1 1 ( ) ( ) ( ) ( 1 ) 1) 2 1 page: 2 2 [ 4 ] [11] ( [11] ) Chapter I 0 n ( n ) (2.1) n + 1 n {n} 0, 1, 2, 3, 4,..., { }, {, { }}, {, { }, {, { }}}, {, { }, {, { }}, {, { }, {, { }}}},... n n =
καθ. Βασίλης Μάγκλαρης
ΣΤΟΧΑΣΤΙΚΕΣ ΔΙΕΡΓΑΣΙΕΣ & ΒΕΛΤΙΣΤΟΠΟΙΗΣΗ Αίθουσα 005 - Νέα Κτίρια ΣΗΜΜΥ Ε.Μ.Π. Ενισχυτική Μάθηση - Δυναμικός Προγραμματισμός: 1. Markov Decision Processes 2. Bellman s Optimality Criterion 3. Αλγόριθμος
Retrieval of Seismic Data Recorded on Open-reel-type Magnetic Tapes (MT) by Using Existing Devices
No. 3 + 1,**- Technical Research Report, Earthquake Research Institute, University of Tokyo, No. 3, pp. + 1,,**-. MT * ** *** Retrieval of Seismic Data Recorded on Open-reel-type Magnetic Tapes (MT) by
Example Sheet 3 Solutions
Example Sheet 3 Solutions. i Regular Sturm-Liouville. ii Singular Sturm-Liouville mixed boundary conditions. iii Not Sturm-Liouville ODE is not in Sturm-Liouville form. iv Regular Sturm-Liouville note
Development of a basic motion analysis system using a sensor KINECT
KINECT 1,a) 2 3,b) KINECT KINECT ( ( Development of a basic motion analysis system using a sensor KINECT Abstract: We developed a basic motion analysis system using a sensor KINECT. Our system estimates
Bayesian Discriminant Feature Selection
1,a) 2 1... DNA. Lasso. Bayesian Discriminant Feature Selection Tanaka Yusuke 1,a) Ueda Naonori 2 Tanaka Toshiyuki 1 Abstract: Focusing on categorical data, we propose a Bayesian feature selection method
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ ΣΧΟΛΗ ΑΓΡΟΝΟΜΩΝ ΚΑΙ ΤΟΠΟΓΡΑΦΩΝ ΜΗΧΑΝΙΚΩΝ ΤΟΜΕΑΣ ΓΕΩΓΡΑΦΙΑΣ ΚΑΙ ΠΕΡΙΦΕΡΕΙΑΚΟΥ ΣΧΕ ΙΑΣΜΟΥ, ΠΕΡΙΟΧΗ ΟΙΚΙΣΤΙΚΩΝ ΚΑΙ ΠΟΛΕΟ ΟΜΙΚΩΝ ΘΕΜΑΤΩΝ ΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ: ΠΟΛΕΟ ΟΜΙΚΕΣ ΚΑΙ ΚΥΚΛΟΦΟΡΙΑΚΕΣ
An Automatic Modulation Classifier using a Frequency Discriminator for Intelligent Software Defined Radio
C IEEJ Transactions on Electronics, Information and Systems Vol.133 No.5 pp.910 915 DOI: 10.1541/ieejeiss.133.910 a) An Automatic Modulation Classifier using a Frequency Discriminator for Intelligent Software
Development of a Tiltmeter with a XY Magnetic Detector (Part +)
No. 2 +0,/,**, Technical Research Report, Earthquake Research Institute, University of Tokyo, No. 2, pp.+0,/,,**,. XY * * ** *** **** ***** Development of a Tiltmeter with a XY Magnetic Detector (Part
Study of In-vehicle Sound Field Creation by Simultaneous Equation Method
Study of In-vehicle Sound Field Creation by Simultaneous Equation Method Kensaku FUJII Isao WAKABAYASI Tadashi UJINO Shigeki KATO Abstract FUJITSU TEN Limited has developed "TOYOTA remium Sound System"
Risk! " #$%&'() *!'+,'''## -. / # $
Risk! " #$%&'(!'+,'''## -. / 0! " # $ +/ #%&''&(+(( &'',$ #-&''&$ #(./0&'',$( ( (! #( &''/$ #$ 3 #4&'',$ #- &'',$ #5&''6(&''&7&'',$ / ( /8 9 :&' " 4; < # $ 3 " ( #$ = = #$ #$ ( 3 - > # $ 3 = = " 3 3, 6?3
ΣΔΥΝΟΛΟΓΗΚΟ ΔΚΠΑΗΓΔΤΣΗΚΟ ΗΓΡΤΜΑ ΗΟΝΗΧΝ ΝΖΧΝ «ΗΣΟΔΛΗΓΔ ΠΟΛΗΣΗΚΖ ΔΠΗΚΟΗΝΧΝΗΑ:ΜΔΛΔΣΖ ΚΑΣΑΚΔΤΖ ΔΡΓΑΛΔΗΟΤ ΑΞΗΟΛΟΓΖΖ» ΠΣΤΥΗΑΚΖ ΔΡΓΑΗΑ ΔΤΑΓΓΔΛΗΑ ΣΔΓΟΤ
[Type the abstract of the document here. The abstract is typically a short summary of the contents of the document. Type the abstract of the document here. The abstract is typically a short summary of
ER-Tree (Extended R*-Tree)
1-9825/22/13(4)768-6 22 Journal of Software Vol13, No4 1, 1, 2, 1 1, 1 (, 2327) 2 (, 3127) E-mail xhzhou@ustceducn,,,,,,, 1, TP311 A,,,, Elias s Rivest,Cleary Arya Mount [1] O(2 d ) Arya Mount [1] Friedman,Bentley
Development of a Seismic Data Analysis System for a Short-term Training for Researchers from Developing Countries
No. 2 3+/,**, Technical Research Report, Earthquake Research Institute, University of Tokyo, No. 2, pp.3+/,,**,. * * Development of a Seismic Data Analysis System for a Short-term Training for Researchers
Re-Pair n. Re-Pair. Re-Pair. Re-Pair. Re-Pair. (Re-Merge) Re-Merge. Sekine [4, 5, 8] (highly repetitive text) [2] Re-Pair. Blocked-Repair-VF [7]
![Re-Pair n. Re-Pair. Re-Pair. Re-Pair. Re-Pair. (Re-Merge) Re-Merge. Sekine [4, 5, 8] (highly repetitive text) [2] Re-Pair. Blocked-Repair-VF [7]](/thumbs/78/77313882.jpg "Re-Pair n. Re-Pair. Re-Pair. Re-Pair. Re-Pair. (Re-Merge) Re-Merge. Sekine [4, 5, 8] (highly repetitive text) [2] Re-Pair. Blocked-Repair-VF [7]")
Re-Pair 1 1 Re-Pair Re-Pair Re-Pair Re-Pair 1. Larsson Moffat [1] Re-Pair Re-Pair (Re-Pair) ( ) (highly repetitive text) [2] Re-Pair [7] Re-Pair Re-Pair n O(n) O(n) 1 Hokkaido University, Graduate School
Yoshifumi Moriyama 1,a) Ichiro Iimura 2,b) Tomotsugu Ohno 1,c) Shigeru Nakayama 3,d)
1,a) 2,b) 1,c) 3,d) Quantum-Inspired Evolutionary Algorithm 0-1 Search Performance Analysis According to Interpretation Methods for Dealing with Permutation on Integer-Type Gene-Coding Method based on
Uniform Convergence of Fourier Series Michael Taylor
Uniform Convergence of Fourier Series Michael Taylor Given f L 1 T 1 ), we consider the partial sums of the Fourier series of f: N 1) S N fθ) = ˆfk)e ikθ. k= N A calculation gives the Dirichlet formula
Vol. 37 ( 2017 ) No. 3. J. of Math. (PRC) : A : (2017) k=1. ,, f. f + u = f φ, x 1. x n : ( ).
Vol. 37 ( 2017 ) No. 3 J. of Math. (PRC) R N - R N - 1, 2 (1., 100029) (2., 430072) : R N., R N, R N -. : ; ; R N ; MR(2010) : 58K40 : O192 : A : 0255-7797(2017)03-0467-07 1. [6], Mather f : (R n, 0) R
Vol. 34 ( 2014 ) No. 4. J. of Math. (PRC) : A : (2014) XJ130246).
Vol. 34 ( 2014 ) No. 4 J. of Math. (PRC) (, 710123) :. -,,, [8].,,. : ; - ; ; MR(2010) : 91A30; 91B30 : O225 : A : 0255-7797(2014)04-0779-08 1,. [1],. [2],.,,,. [3],.,,,.,,,,.., [4].,.. [5] -,. [6] Markov.
Additional Results for the Pareto/NBD Model
Additional Results for the Pareto/NBD Model Peter S. Fader www.petefader.com Bruce G. S. Hardie www.brucehardie.com January 24 Abstract This note derives expressions for i) the raw moments of the posterior
ΑΛΓΟΡΙΘΜΟΙ Άνοιξη I. ΜΗΛΗΣ
ΑΛΓΟΡΙΘΜΟΙ http://eclass.aueb.gr/courses/inf161/ Άνοιξη 2016 - I. ΜΗΛΗΣ ΔΥΝΑΜΙΚΟΣ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΣ ΑΛΓΟΡΙΘΜΟΙ - ΑΝΟΙΞΗ 2016 - Ι. ΜΗΛΗΣ 08 DP I 1 Dynamic Programming Richard Bellman (1953) Etymology (at
Answers - Worksheet A ALGEBRA PMT. 1 a = 7 b = 11 c = 1 3. e = 0.1 f = 0.3 g = 2 h = 10 i = 3 j = d = k = 3 1. = 1 or 0.5 l =
C ALGEBRA Answers - Worksheet A a 7 b c d e 0. f 0. g h 0 i j k 6 8 or 0. l or 8 a 7 b 0 c 7 d 6 e f g 6 h 8 8 i 6 j k 6 l a 9 b c d 9 7 e 00 0 f 8 9 a b 7 7 c 6 d 9 e 6 6 f 6 8 g 9 h 0 0 i j 6 7 7 k 9
Εξοικονόμηση Ενέργειας σε Εγκαταστάσεις Δρόμων, με Ρύθμιση (Dimming) ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ ΣΧΟΛΗ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΜΗΧΑΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΗΛΕΚΤΡΙΚΗΣ ΙΣΧΥΟΣ Εξοικονόμηση Ενέργειας σε Εγκαταστάσεις Δρόμων, με Ρύθμιση (Dimming) ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ Νικηφόρος
2 ~ 8 Hz Hz. Blondet 1 Trombetti 2-4 Symans 5. = - M p. M p. s 2 x p. s 2 x t x t. + C p. sx p. + K p. x p. C p. s 2. x tp x t.
36 2010 8 8 Vol 36 No 8 JOURNAL OF BEIJING UNIVERSITY OF TECHNOLOGY Aug 2010 Ⅰ 100124 TB 534 + 2TP 273 A 0254-0037201008 - 1091-08 20 Hz 2 ~ 8 Hz 1988 Blondet 1 Trombetti 2-4 Symans 5 2 2 1 1 1b 6 M p
; +302 ; +313; +320,.
1.,,*+, - +./ +/2 +, -. ; +, - +* cm : Key words: snow-water content, surface soil, snow type, water permeability, water retention +,**. +,,**/.. +30- +302 ; +302 ; +313; +320,. + *+, *2// + -.*, **. **+.,
Q π (/) ^ ^ ^ Η φ. <f) c>o. ^ ο. ö ê ω Q. Ο. o 'c. _o _) o U 03. ,,, ω ^ ^ -g'^ ο 0) f ο. Ε. ιη ο Φ. ο 0) κ. ο 03.,Ο. g 2< οο"" ο φ.
II 4»» «i p û»7'' s V -Ζ G -7 y 1 X s? ' (/) Ζ L. - =! i- Ζ ) Η f) " i L. Û - 1 1 Ι û ( - " - ' t - ' t/î " ι-8. Ι -. : wî ' j 1 Τ J en " il-' - - ö ê., t= ' -; '9 ',,, ) Τ '.,/,. - ϊζ L - (- - s.1 ai
Homomorphism in Intuitionistic Fuzzy Automata
International Journal of Fuzzy Mathematics Systems. ISSN 2248-9940 Volume 3, Number 1 (2013), pp. 39-45 Research India Publications http://www.ripublication.com/ijfms.htm Homomorphism in Intuitionistic
Πιθανολογική Ανάλυση Αποφάσεων. Συστήματα Αποφάσεων Εργαστήριο Συστημάτων Αποφάσεων και Διοίκησης
Πιθανολογική Ανάλυση Αποφάσεων Αβεβαιότητα Known knowns Ποσοτικοποιήσιμη Πιθανότητα Known unknowns Εκτίμηση ενδεχομένου Unknown unknowns Αρνητική επίδραση Ρίσκο Black Swan Πιθανολογική Προσέγγιση Θεωρούμε
ES440/ES911: CFD. Chapter 5. Solution of Linear Equation Systems
ES440/ES911: CFD Chapter 5. Solution of Linear Equation Systems Dr Yongmann M. Chung http://www.eng.warwick.ac.uk/staff/ymc/es440.html Y.M.Chung@warwick.ac.uk School of Engineering & Centre for Scientific
Χρηματοοικονομική Ανάπτυξη, Θεσμοί και
ΑΡΙΣΤΟΤΕΛΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ ΣΧΟΛΗ ΝΟΜΙΚΩΝ, ΟΙΚΟΝΟΜΙΚΩΝ ΚΑΙ ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΤΜΗΜΑ ΟΙΚΟΝΟΜΙΚΩΝ ΕΠΙΣΤΗΜΩΝ Τομέας Ανάπτυξης και Προγραμματισμού Χρηματοοικονομική Ανάπτυξη, Θεσμοί και Οικονομική
Η νοθεία των τροφίμων, μελέτη περίπτωσης η νοθεία του ελαιόλαδου
ΣΧΟΛΗ ΤΕΧΝΟΛΟΓΙΑΣ ΓΕΩΠΟΝΙΑΣ ΤΜΗΜΑ ΤΕΓΕΠ (Τεχνολογίας Γεωργικών Προϊόντων) ΑΤΕΙ ΚΑΛΑΜΑΤΑΣ Η νοθεία των τροφίμων, μελέτη περίπτωσης η νοθεία του ελαιόλαδου Σπουδάστρια : ΓΕΩΡΓΙΑΔΟΥ ΔΕΣΠΟΙΝΑ Εισηγητής καθηγητής
Medium Data on Big Data
IT 17081 Examensarbete 15 hp November 2017 Medium Data on Big Data Predicting Disk Failures in CERNs NetApp-based Data Storage System Albin Stjerna Institutionen för informationsteknologi Department of
Vol. 34 ( 2014 ) No. 4. J. of Math. (PRC) : A : (2014) Frank-Wolfe [7],. Frank-Wolfe, ( ).
![Vol. 34 ( 2014 ) No. 4. J. of Math. (PRC) : A : (2014) Frank-Wolfe [7],. Frank-Wolfe, ( ).](/thumbs/91/105872052.jpg "Vol. 34 ( 2014 ) No. 4. J. of Math. (PRC) : A : (2014) Frank-Wolfe [7],. Frank-Wolfe, ( ).")
Vol. 4 ( 214 ) No. 4 J. of Math. (PRC) 1,2, 1 (1., 472) (2., 714) :.,.,,,..,. : ; ; ; MR(21) : 9B2 : : A : 255-7797(214)4-759-7 1,,,,, [1 ].,, [4 6],, Frank-Wolfe, Frank-Wolfe [7],.,,.,,,., UE,, UE. O-D,,,,,
Comparison of Evapotranspiration between Indigenous Vegetation and Invading Vegetation in a Bog
J. Jpn. Soc. Soil Phys. No. +*-, p.-3.1,**0 ** * *** Comparison of Evapotranspiration between Indigenous Vegetation and Invading Vegetation in a Bog Toshiki FUJIMOTO*, Ippei IIYAMA*, Mai SAKAI*, Osamu
Πανεπιστήµιο Μακεδονίας Τµήµα ιεθνών Ευρωπαϊκών Σπουδών Πρόγραµµα Μεταπτυχιακών Σπουδών στις Ευρωπαϊκές Πολιτικές της Νεολαίας
Πανεπιστήµιο Μακεδονίας Τµήµα ιεθνών Ευρωπαϊκών Σπουδών Πρόγραµµα Μεταπτυχιακών Σπουδών στις Ευρωπαϊκές Πολιτικές της Νεολαίας Πρόγραµµα εργασίας «Εκπαίδευση και Κατάρτιση 2020» της Ε.Ε: Στρατηγικές δια
ΔΙΑΤΜΗΜΑΤΙΚΟ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΣΤΗ ΔΙΟΙΚΗΣΗ ΕΠΙΧΕΙΡΗΣΕΩΝ ΘΕΜΕΛΙΩΔΗΣ ΚΛΑΔΙΚΗ ΑΝΑΛΥΣΗ ΤΩΝ ΕΙΣΗΓΜΕΝΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ ΤΗΣ ΕΛΛΗΝΙΚΗΣ ΑΓΟΡΑΣ
ΔΙΑΤΜΗΜΑΤΙΚΟ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΣΤΗ ΔΙΟΙΚΗΣΗ ΕΠΙΧΕΙΡΗΣΕΩΝ Διπλωματική Εργασία ΘΕΜΕΛΙΩΔΗΣ ΚΛΑΔΙΚΗ ΑΝΑΛΥΣΗ ΤΩΝ ΕΙΣΗΓΜΕΝΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ ΤΗΣ ΕΛΛΗΝΙΚΗΣ ΑΓΟΡΑΣ Του ΚΩΣΤΟΥΛΗ ΔΗΜΗΤΡΙΟΥ ΤΟΥ ΒΑΣΙΛΕΙΟΥ
Σχέση στεφανιαίας νόσου και άγχους - κατάθλιψης
Τρίμηνη, ηλεκτρονική έκδοση του Τμήματος Νοσηλευτικής Α, Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας _ΑΝΑΣΚΟΠΗΣΗ_ Πολυκανδριώτη Μαρία 1, Φούκα Γεωργία 2 1. Καθηγήτρια Εφαρμογών Νοσηλευτικής Α, ΤΕΙ Αθήνας 2.
CSJ. Speaker clustering based on non-negative matrix factorization using i-vector-based speaker similarity
i-vector 1 1 1 1 i-vector CSJ i-vector Speaker clustering based on non-negative matrix factorization using i-vector-based speaker similarity Fukuchi Yusuke 1 Tawara Naohiro 1 Ogawa Tetsuji 1 Kobayashi
情報処理学会研究報告 IPSJ SIG Technical Report Vol.2014-MUS-104 No /8/26 1,a) Music Structure and Composition with Sound Directivity in 3D Space
1,a) 1 1 2 2. Music Structure and Composition with Sound Directivity in 3D Space Riki Ishino 1,a) Yusuke Naka 1 Yuya Yoshida 1 Yusuke Matsui 2 Tomoko Yonezawa 2 Abstract: In this research, we propose a
k A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +
[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +](/thumbs/73/69566903.jpg "k A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +")
Chapter 3. Fuzzy Arithmetic 3- Fuzzy arithmetic: ~Addition(+) and subtraction (-): Let A = [a and B = [b, b in R If x [a and y [b, b than x+y [a +b +b Symbolically,we write A(+)B = [a (+)[b, b = [a +b
Μεταπτυχιακή εργασία : Μελέτη της εξέλιξης του προσφυγικού οικισμού της Νέας Φιλαδέλφειας με χρήση μεθόδων Γεωπληροφορικής.
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ Σχολή Αγρονόμων & Τοπογράφων Μηχανικών ΜΕΤΑΠΤΥΧΙΑΚΟ ΠΡΟΓΡΑΜΜΑ ΓΕΩΠΛΗΡΡΟΦΟΡΡΙΙΚΗ Μεταπτυχιακή εργασία : Μελέτη της εξέλιξης του προσφυγικού οικισμού της Νέας Φιλαδέλφειας με
Numerical Analysis FMN011
Numerical Analysis FMN011 Carmen Arévalo Lund University carmen@maths.lth.se Lecture 12 Periodic data A function g has period P if g(x + P ) = g(x) Model: Trigonometric polynomial of order M T M (x) =
Study on Re-adhesion control by monitoring excessive angular momentum in electric railway traction
() () Study on e-adhesion control by monitoring excessive angular momentum in electric railway traction Takafumi Hara, Student Member, Takafumi Koseki, Member, Yutaka Tsukinokizawa, Non-member Abstract
Assalamu `alaikum wr. wb.
LUMP SUM Assalamu `alaikum wr. wb. LUMP SUM Wassalamu alaikum wr. wb. Assalamu `alaikum wr. wb. LUMP SUM Wassalamu alaikum wr. wb. LUMP SUM Lump sum lump sum lump sum. lump sum fixed price lump sum lump
1. Panel Data.
- / / / (-) / : / : -.... - - δ. -. e-mail:karnameh@yahoo.com e-mail:nemata44@yahoo.com 1. Panel Data. ... -, : /.,......... ( )., (-) :...... 1. Convergence. ........... "..(,)...... /......(, ),...,..........
ΕΚΠ 413 / ΕΚΠ 606 Αυτόνοµοι (Ροµ οτικοί) Πράκτορες
ΕΚΠ 413 / ΕΚΠ 606 Αυτόνοµοι (Ροµ οτικοί) Πράκτορες Μερική Παρατηρησιµότητα Θεωρία Παιγνίων Τµήµα Ηλεκτρονικών Μηχανικών και Μηχανικών Υ ολογιστών Πολυτεχνείο Κρήτης Ε ανάληψη Reinforcement Learning (RL)