Inference Clause Samples
The Inference clause establishes how information, facts, or conclusions may be drawn from available evidence or circumstances within the context of the agreement. Typically, it allows parties or adjudicators to make reasonable assumptions or deductions based on the conduct of the parties, the documentation provided, or the surrounding circumstances, even if explicit statements are not present. This clause is essential for addressing situations where not every detail can be expressly stated, ensuring that logical conclusions can be reached to interpret obligations or resolve disputes.
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Inference. All Parties have had the opportunity to review this Agreement prior to execution, and no adverse inference shall be made against the party drafting this Agreement in any dispute over the interpretation of any provision herein.
Inference. Again, while not conclusive by itself, this data suggests that the Existing Site has experienced a twelve percent (12%) decrease vs. expectation during the Post Period due to localized factors specific to the Existing Site trade area, and not due to DMA-wide variables. Multi-State Disclosure Document Control No. 040114 Exhibit E to Procedures for Resolving Disputes Relating to the Development of New Restaurants If there appears to be an impact on the Existing Site that is due to factors within its trade area, rather than due to broader, DMA-wide trends (Example 2 above), identify all significant factors that may have contributed to this impact in addition to the New Restaurant. These could include but may not be limited to the following:
Inference. Many of the Existing Site’s customers will be drawn to the traffic generators in the new center, and allocate some of their limited eating out dollars to whatever restaurant choices are available when they are there, since it is convenient. All or a substantial portion of the 8% drop in sales can reasonably be attributed to the new center. Multi-State Disclosure Document Control No. 040114 Exhibit E to Procedures for Resolving Disputes Relating to the Development of New Restaurants Example 4: Same as Example 3, except that a new EPL Restaurant opens at the new Power Center 6 months after the last anchor tenant opens. Existing Site’s sales then decline further, to a 12% overall decline vs. before the Power Center opened, as follows: 12 months prior to New Power Center opening $ 83,333 — First 6 months New Power Center open $ 76,666 (8 %) Next 12 months New Restaurant Open $ 73,333 (12 %) Inference: It would appear that the majority of the decline is due to the existence of the new Power Center (8%), and that only about 4% is due to the New Restaurant (= 12%-8%).
Inference the relation can be identified and classified through inference via other existing relations (e.g. two events linked to two different timexes can be ordered by means of the comparison of the values of timexes only).
Inference. As discussed in Section 2.1.1, we optimize the negative log marginal likelihood. For the implementation this means we need a likelihood, a prior and data to perform the inference.
i⋅ Remember that the covariance function is evaluated at all pairs of rows X of X to create the covariance matrix Kij = k(Xi⋅, Xj⋅, θ). The log marginal likelihood of the inference can be written as (Eq. (2.1)) log p Y X, θ, σ ( | 2) = (( (( )N | + 2 |)– 1 ) (− 1 t( +
Inference. No inference shall be drawn in favor of or against any party based on its participation in the drafting of this Agreement. [Signature Page Follows] Elk Ridge, Inc. (7/7/05) 22 CONFIDENTIAL MATERIAL HAS BEEN OMITTED AND FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION. BOXES AND ASTERISKS DENOTE SUCH OMISSION.
Inference. The given table presents the results of t-tests conducted on various aspects related to academic responsibilities and work environment in academia. Here are the inferences drawn from the provided data:
Inference. Many of the Existing Site's customers will be drawn to the traffic generators in the new center, and allocate some of their limited eating out dollars to whatever restaurant choices are available when they are there, since it is convenient. All or a substantial portion of the 8% drop in sales can reasonably be attributed to the new center. Example 4: Same as Example 3, except that a new EPL Restaurant opens at the new Power Center 6 months after the last anchor tenant opens. Existing Site's sales then decline further, to a 12% overall decline vs. before the Power Center opened, as follows: 12 months prior to New Power Center opening $ 83,333 — First 6 months New Power Center open $ 76,666 (8 )% Next 12 months New Restaurant Open $ 73,333 (12 )% Inference: It would appear that the majority of the decline is due to the existence of the new Power Center (8%), and that only about 4% is due to the New Restaurant (= 12%- 8%).
Inference. These methodological issues will be described one by one in this report. Bethlehem, J. (2009) The rise of survey sampling, Discussion paper 09015, Statistics Netherlands. ▇▇▇▇▇▇▇▇, ▇ et al (2016) The role of administrative data in the big data revolution in social science research ▇▇▇▇▇▇, ▇.▇. (2013) Is the Sky Falling? New Technology, Changing Media, and the Future of Surveys. Survey Research Methods 7(3), pp. 145-156. ▇▇▇▇, ▇.▇.▇., ▇▇▇▇, M.J.H. (2014) Big data as a Source of Statistical Information. The Survey Statistician 69, pp. 22-31 ▇▇▇▇▇▇▇, M., ▇▇▇▇▇▇▇▇▇, ▇., ▇▇▇▇▇▇▇, V., ▇▇▇▇, P., ▇▇▇▇▇▇▇▇▇, M., ▇▇▇▇, A. (2013) What does "Big Data" mean for Official Statistics? Paper for the High-Level Group for the Modernization of Statistical Production and Services, March 10. ▇▇▇▇, ▇. (1970) The Structure of Scientific Revolutions. University of Chicago Press.
Inference. To define optimal estimation, we consider a loss function. The loss function is the loss (or cost, risk) that occurs when the correct label is t, but the estimated output by the mathematical model is tˆ. It is denoted as a non-negative function l(t, tˆ). Examples include quadratic loss l2(t, tˆ) = (t − tˆ)2 and 0-1 loss l0(t, tˆ) = |t − tˆ|0 = 1(for t − tˆ 0), |t − tˆ|0 = 0(for t − tˆ = 0). Once the loss function is chosen, the optimal prediction tˆ(x) is chosen to minimize the generalization loss. To learn the model pD(t|x) earned for a given data set D as close as possible to the true predictive distribution p(t|x) from a given training set D, machine learning is performed by first selecting a group of parametric probabilistic models, also called hypothesis classes, and then learning the parameters of the model to fit D.