'1 max' diaporamas de présentation

? 8 ? ?? ??

? 8 ? ?? ??. ??? ????. ????. ? 1 ?????????????????????????????????????????????????????????????????????? ? 2 ???????????? ? 3 ??????????????????????????????????????????????????????????????????????????????????????????????. ????.

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Kainan University ?????? , ? ? ?

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Monte-Carlo Planning II

Monte-Carlo Planning II. Alan Fern. Outline. Preliminaries: Markov Decision Processes What is Monte-Carlo Planning? Uniform Monte-Carlo Single State Case (UniformBandit) Policy rollout Approximate Policy Iteration Sparse Sampling Adaptive Monte-Carlo Single State Case (UCB Bandit)

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Advisor: Prof. R. C. T. Lee Speaker: L. C. Chen

Finding approximate palindromes in strings Pattern Recognition, vol.35, pp. 2581-2591, 2002 Alexandre H. L Porto and Valmir C. Barbosa. Advisor: Prof. R. C. T. Lee Speaker: L. C. Chen. Definition. S: a string of n characters. S [ i ]: the i th character in S .

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Temporal Probabilistic Models

Temporal Probabilistic Models. Motivation. Observing a stream of data Monitoring (of people, computer systems, etc ) Surveillance, tracking Finance & economics Science Questions: Modeling & forecasting Unobserved variables. Agenda. Markov models Hidden Markov Models

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Hidden Markov Models

550 views • 44 slides

Hidden Markov Models

509 views • 41 slides

??????????? 10 ? ??????? 2013 ????????? ??????? 18:30 ? ???????? 4 ? 502d ???????

??????????? 10 ? ??????? 2013 ????????? ??????? 18:30 ? ???????? 4 ? 502d ??????? ????? http://www.mbtech.sdu.edu.cn/biocomp/. ??????. ????. ??????. ???? Dotlet: http://myhits.isb-sib.ch/cgi-bin/dotlet. ??????. ???? Dotlet: http://myhits.isb-sib.ch/cgi-bin/dotlet.

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Hidden Markov Models

486 views • 40 slides

Hidden Markov Models

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COMPUTATIONAL TECHNIQUES

COMPUTATIONAL TECHNIQUES. 1.6 Computational Techniques. Some details on the Simplex Method approach 2x2 games 2x n and m x2 games Recall : First try pure strategies. If there are no saddle points use mixed strategies. Linear Programming. 1.6.1 Example (page 32).

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Hidden Markov Models

Hidden Markov Models. Lecture 5, Tuesday April 15, 2003. Review of Last Lecture. Lecture 2, Thursday April 3, 2003. Time Warping. Definition: ?(u), ?(u) are connected by an approximate continuous time warping (u 0 , v 0 ), if:

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Hidden Markov Models

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HW14

HW14. 100071021. Range Forward. function [ K2,price ] = blsranfow ( F,K1,price,r,rf,T,sigma ) [temp,f1] = blsprice (F,K1,r,T,sigma,rf); price = max(price,0); f2 = price + f1; tryK = K1; found = 0; while 1 Vec = tryK-K1:0.0001:tryK; tryV = blsprice ( F,Vec,r,T,sigma,rf );.

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Max International Overview

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HW6

HW6. 100071021 ???. Proportion Dividends (1). function [ Cprice , Pprice , Clattice , Plattice ] = bitprice_p ( S, K, r, T, sigma, q, tau, N, mode ) % mode : 1=>American 0=>European % dividend : proportion deltaT = T/N; u = exp ( sigma * sqrt ( deltaT ) ); d = 1/u;

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????????? hw10

????????? hw10. ????? ???. Outline. 1.CBValue - Putable -Callable - Putable + Callable 2. - Rand+BoxMuller - Halton+BoxMuller:Eur Put. Putable. function [ price,lattice ] = CBValuePut ( s0,r,T,sigma,N,Q,I,P,tau ) deltaT =T/N; u=exp(sigma* sqrt ( deltaT )); d=1/u;

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HW9

HW9. 100071021 ???. Convertible Bonds. function [ price, lattice] = CBprice ( S, I, Q, r, T, sigma, N) deltaT = T/N; u = exp ( sigma * sqrt ( deltaT ) ); d = 1/u; p = ( exp( r * deltaT ) - d ) / ( u - d ); for i = 0 : N

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1. ???? ???? ???? ????? N ?? ?? ???? ( ? 1000 ??? ) ???????? ?? NA ??

1. ???? ???? ???? ????? N ?? ?? ???? ( ? 1000 ??? ) ???????? ?? NA ?? ?????? FindMAX ?????? NA ?? ??? ??????. int N=5; int FindMAX(){ m = ? 1 ; // max i = 1; while(i<=N){ if(NA[i]>m) swap (&m,&NA[i]); i++; } return m; }. FindMAX m ? ? 1 i ? 1 ? i <= N ? NA[i] > m

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Viper ? Review Forecasts

Viper ? Review Forecasts. Viper ? Reports ? FMT Report. USDA NRCS National Water & Climate Center * - DATA CURRENT AS OF: April 03, 2008 08:48:51 AM - Based on April 01, 2008 forecast values SOUTHERN OREGON

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