How To make Approximate Equivalents
MEASURE EQUIVALENTS:
3 ts 1 tb
16 tb 1 c
1/4 c 4 tb
1/3 c 5-1/3 tb
2 c 1 pt
4 c 1 qt
2 pt 1 qt
1 1/2 fl 1 jigger
WEIGHT EQUIVALENTS:
1 oz 25 gm
1/16 oz 1 gm; 0.035 oz
1 oz 28.35 gm
1 lb 453.6 grams
2 1/4 lb 1 kg
PRODUCT EQUIVALENTS:
3 1/2 c 1 lb brown sugar
2 1/4 c 1 lb granulated sugar
3 3/4 c 1 lb powdered sugar
2 c 1 lb butter
2 c 1 lb shortening
4 1/2 c 1 lb cheese; grated
3 3/4 c 1 lb flour
3 1/3 c 1 lb whole wheat flour
3 1/4 c 1 lb corn meal
3 c 1 lb raisins, seeded
2 2/3 c 1 lb dates, pitted
3 1/2 c 1 lb dates, unpitted
CAN EQUIVALENTS:
1 1/2 c #1 can
2 1/2 c #2 can
3 1/2 c #2-1/2 can
4 c #3 can
13 c #10 can
OTHER EQUIVALENTS:
4 1/2 c 3 lb chicken, cooked/diced
-(1-1/2 lb) 2 tb Cocoa = 1 chocolate square
1 c Uncooked macaroni = 2-2/3
-c cooked 1 lb Uncooked meat = 2-2/3 cooked
1 c Uncooked rice = 4 c cooked
1 c Uncooked spaghetti=2c cooked
How To make Approximate Equivalents's Videos
How to Calculate the Cost Price Easy Trick
In this video i am showing the easy way to find the cost price of an item if profit and selling price is given. The percentage of profit is already know this example. This method will apply if profit percentage is given. Cost price is really important in business, retail industry , financial industry and even for math and finance students, so in this video i make it simple to calculate.
#costprice#sellingprice#profit
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UC Irvine CEE-290: Topic 7 (Approximate Bayesian Computation)
Topics that will be addressed include:
1. What is diagnostic model evaluation?
2. Why diagnostic model evaluation?
3. Classical likelihood functions mix and dilute information
4. Medical diagnostics
5. Back pain
6. The diagnostic approach
7. Likelihood free inference
8. Approximate Bayesian computation
9. Approximate Bayesian computation with summary statistcs
10. How to select value for epsilon?
11. How to sample ABC posterior distribution?
12. Rejection sampling
13. Population Monte Carlo sampling
14. Markov chain Monte Carlo simulation with DREAM_(ABC)
15. Benchmark studies PMC - DREAM_(ABC)
16. Case study: diagnostic model evaluation
17. Runoff index
18. Recession analysis
19. Flow duration curve: Closed-form equation
20. Byproduct: new method for geophysical inversion
21. Byproduct: new method to help detect system nonstationarity
Webinar: Rounding and approximation: Uses in real life | Maths Week series
In this webinar Mary looks at how rounding and approximation can be used to make everyday calculations simpler, and data easier to understand. She also shows some practical examples used in real life, showing why being exact with figures is not always the optimal way to do things!
CCN 2019: Tutorial T-C: Approximate inference in the brain: free energy, sampling, and beyond
2019 Conference on Cognitive Computational Neuroscience
13-16 September 2019, Berlin, Germany
Tutorial T-C Approximate inference in the brain: free energy, sampling, and beyond
Presented by Sam Gershman
Luis Scoccola Approximate and Discrete Vector Bundles in Theory and Applications
April 16th, 2021 Applied Topology in Albany (ATiA) Seminar
Speaker: Luis Scoccola (Michigan State University)
Title: Approximate and Discrete Vector Bundles in Theory and Applications
Abstract: Synchronization problems, such as the problem of reconstructing a 3D shape from a set of 2D projections, can often be modeled by principal bundles. Similarly, the application of local PCA to a point cloud concentrated around a manifold approximates the tangent bundle of the manifold. In the first case, the characteristic classes of the bundle provide obstructions to global synchronization, while, in the second case, they provide topological information of the manifold beyond its homology, and in particular, give obstructions to dimensionality reduction. I will describe joint work with Jose Perea in which we propose several notions of approximate and discrete vector bundle, study the extent to which they determine true vector bundles, and give algorithms for the stable and consistent computation of low dimensional characteristic classes directly from these combinatorial representations. No previous knowledge of the theory of vector bundles will be assumed.