'Model parameters' diaporamas de présentation

I NTRODUCTION TO MANAGERIAL DECISION MODELING

I NTRODUCTION TO MANAGERIAL DECISION MODELING. OBJECTIVES. Define management science Define and classify decision models List and explain steps involved in developing decision models Remind breakeven analysis with computer applications

877 views • 59 slides

I NTRODUCTION TO MANAGERIAL DECISION MODELING

I NTRODUCTION TO MANAGERIAL DECISION MODELING. Objectives. Define management science Define decision model and describe its importance Classify decision models List and explain steps involved in developing decision models in practice Remind breakeven analysis with computer applications

1.09k views • 59 slides

927 views • 57 slides

Markov Model

611 views • 45 slides

DCM for evoked responses

DCM for evoked responses. Harriet Brown SPM for M/EEG course, 2013. The DCM analysis pathway. The DCM analysis pathway. Build model(s). Fit your model parameters to the data. Pick the best model. Make an inference (conclusion). Collect data. The DCM analysis pathway. Build model(s).

636 views • 45 slides

Image-based Material Retrieval

Image-based Material Retrieval. Rui Wang Dept. of Computer Science Univ. of North Carolina at Chapel Hill. Outline. Motivation Study of physical system Examples Organ elastic parameter retrieval Cloths elastic parameter retrieval Limitations and open research issues. Motivation.

600 views • 43 slides

Clustering III

Clustering III. Lecture outline. Soft (model-based) clustering and EM algorithm Clustering aggregation [A. Gionis , H. Mannila , P. Tsaparas : Clustering aggregation, ICDE 2004] Impossibility theorem for clustering [Jon Kleinberg, An impossibility theorem for clustering, NIPS 2002].

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a tutorial on Markov Chain Monte Carlo (MCMC)

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CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011 Oregon Health & Science University Center for Sp

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011 Oregon Health & Science University Center for Spoken Language Understanding John-Paul Hosom Lecture 14 February 23 Language Models. Language Models.

535 views • 32 slides

T.A. Blasingame, Texas A&M U. Department of Petroleum Engineering Texas A&M University College Station, TX 77843

737 views • 32 slides

Environmental Remote Sensing GEOG 2021

580 views • 28 slides

Automating Analyses with ModelBuilder

527 views • 23 slides

An approach for prediction of coil specific forming limit curves

An approach for prediction of coil specific forming limit curves. Timo Manninen, Mikko Palosaari Outokumpu, Process R&D, Tornio, Finland Jari Larkiola Centre of Advanced Steel Research University of Oulu, Oulu, Finland. ESSC & DUPLEX 2019. Wien, 30th of September 2019. Contents.

775 views • 23 slides

EM Algorithm

EM Algorithm. Jur van den Berg. Kalman Filtering vs. Smoothing. Dynamics and Observation model Kalman Filter: Compute Real-time, given data so far Kalman Smoother: Compute Post-processing, given all data. EM Algorithm. Kalman smoother:

394 views • 23 slides

Bootstrap for Goodness of Fit

Bootstrap for Goodness of Fit. G. Jogesh Babu Center for Astrostatistics http://astrostatistics.psu.edu. Astrophysical Inference from astronomical data. Fitting astronomical data Non-linear regression Density (shape) estimation Parametric modeling Parameter estimation of assumed model

428 views • 21 slides

Pairs Trading Analysis

Pairs Trading Analysis. Global Asset Allocation Spring 2004 The Hemline Theorists. Pairs Trading Analysis. Introduction Developing the Model Results. Introduction. Long/short, market- and industry-neutral strategy Pair selection criteria: same industry mature companies

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CFC-12 and oxygen data from two UW student cruises in the eastern North Pacific

437 views • 16 slides

Managing Resistance Evolution with Refugia

Managing Resistance Evolution with Refugia. Livingston, Carlson and Fackler Presented by Ben Crost. The Setting. Cotton production in the midsouth Two pests: budworm and bollworm Two pest-control technologies: Bt-cotton and pyrethroids

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Detecting Subtle Sequence Signals: a Gibbs Sampling Strategy for Multiple Alignment

Detecting Subtle Sequence Signals: a Gibbs Sampling Strategy for Multiple Alignment. Lawrence, Altschul, Boguski, Liu, Neuwald, Wootton, Science, 1993. Motif Finding Problem.

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Capillary Pressure: Reservoir Seal Capillary Pressure / Saturation Relationship (S w * Model)

Capillary Pressure: Reservoir Seal Capillary Pressure / Saturation Relationship (S w * Model). Fault (impermeable). Oil/water contact (OWC). Migration route. Seal. Reservoir rock. Hydrocarbon accumulation in the reservoir rock. Source rock. Reservoir Seal. Seal. Seal.

462 views • 11 slides